Note: Descriptions are shown in the official language in which they were submitted.
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DETECTION AND PREDICTION OF INFECTIOUS DISEASE
CROSS REFERENCES TO RELATED APPLICATIONS
[0001] This application claims priority to and the benefit of U.S.
Provisional
Application No. 62/770,182, titled "Detection and Prediction of Infectious
Disease", filed
November 21, 2018, U.S. Provisional Application No.62/770,181, titled "Direct-
to-Library
Methods, Systems and Compositions", filed November 21, 2018 and U.S.
Provisional
Application No. 62/849,618, titled "Fragment Length Distributions and Methods
of Using
Such", filed May 17, 2019, the entire contents of which are hereby
incorporated by reference
for all purposes.
FIELD OF INVENTION
[0002] The present invention relates to the use of fragment length
distributions in
nucleic acid libraries to identify microbes, identify the type of host-microbe
biological
interaction, identify infection sites or site of localization, select the
therapy or treatment,
monitor treatment, monitor cytotoxicity, detect transplant rejection, monitor
immune system
response or activity, identify stage of infection, monitor transplant
rejection and for use in
cancer diagnostics.
BACKGROUND
[0003] For many microbial infections, the first stage is colonization. In
some cases, a
microbial infection can progress to persistent infection and may develop into
an invasive
disease stage. Examples of microbes that can develop into invasive disease
include
Cytomegalovirus, Epstein-Barr virus, Heliobacter pylon, Clostridium difficile,
certain
sexually-transmitted infections, and others. For patients infected with these
types of
microorganisms identifying an infection at the correct stage, colonization
stage or invasive
stage, can be an important factor in making effective treatment decisions.
Site of localization
also may impact the significance and available treatment options. Some microbe
related
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diseases occur in the absence of what is considered typical colonization. For
example, C.
botulinum ingestion can be sufficient to cause symptoms.
[0003] Furthermore, infections at the invisible stage often present with
no symptoms
or non-specific symptoms that may resemble multiple other diseases.
Consequently, such
infections are often undiagnosed, misdiagnosed or treated symptomatically
allowing the
microorganism to persist and increasing the risk that the patient's infection
will progress to
invasive disease.
[0004] Helicobacter pylon (H. pylon) is the most common chronic bacterial
infection
in humans. It is estimated that 50% of the worlds population is infected. In
the United States,
approximately 30% of adults are infected by age 50 with the majority of
individuals infected
during childhood. Chen, Y. and M.J. Blaser, J Infect Dis, 2008. 198(4): p. 553-
60. There are
strong associations between H. pylon infection and gastrointestinal (GI)
conditions, including
chronic gastritis, peptic ulcer disease, gastric adenocarcinoma, and lymphoma.
Peptic ulcer
disease (PUD) is the most common manifestation of H. pylon infection and has
an annual
incidence of 0.1-0.19% for physician-diagnosed PUD. Sung, J.J., E.J. Kuipers,
and H.B. El-
Serag, Aliment Pharmacol Ther, 2009. 29(9): p. 938-46. It is estimated that
infected
individuals have a lifetime risk of 10-20% of developing peptic ulcer disease.
Kuipers, E.J., et
al., Aliment Pharmacol Ther, 1995. 9 Suppl 2: p. 59-69.
[0005] The primary phenomenon responsible for the initiation of these
disease
manifestations is mucosal inflammation in response to the presence of H.
pylon. However,
only a small percentage of individuals with H. pylon will have inflammation
associated with
invasive H. pylon disease.
[0006] Currently, it is challenging to distinguish between patients who
have an H.
pylon invisible infection stage versus those who have a symptomatic stage or
are at risk to
progress to a symptomatic stage. While most infections with H. pylon are
asymptomatic,
patients with invasive disease may begin experiencing symptoms of persistent
dyspepsia, such
as abdominal pain, nausea or vomiting and lack of appetite. However, these non-
specific
symptoms could also be caused by other conditions and are experienced by
healthy people.
Some physicians will test all patients with unexplained persistent dyspepsia.
Other physicians
follow current guidelines, which recommend testing for H. pylon in individuals
with active
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PUD, a history of documented peptic ulcer, or gastric MALT lymphoma. Chey,
W.D., et al.,
Am J Gastroenterol, 2007. 102(8): p. 1808-25. Thus, physicians following
guidelines will
only test patients with a high probability of H. pylori-associated disease,
which could lead to
undertreatment.
[0007] There are currently several methods to test for H. pylon. The
existing non-
invasive testing methods for H. pylon include stool antigen testing, urea
breath test, and H.
pylon serology. However, these methods can determine only whether H. pylon is
present, but
not whether there is H. pylon invasion or associated inflammation. Some
practitioners will
initiate primary treatment for eradication based on a positive result from one
of the non-
invasive tests, which may lead to overtreatment.
[0008] The current gold standard for diagnosing H. pylon disease is to
perform an
upper endoscopy to document (via biopsy) specific pathologic changes due to H.
pylori
invasion such as inflammation, atrophy, and intestinal metaplasia in
conjunction with
detection of H. pylon in biopsy samples. Dixon, M.F., et al., Helicobacter,
1997. 2 Suppl 1: p.
S17-24. However, there are serious risks and potential complications from this
procedure,
including bleeding which can sometimes require a transfusion, infection, and
tearing of the GI
tract.
[0009] Overall, about 75% of patients that comply with primary treatment
of H. pylori
infection are considered cured after the first treatment, based on a negative
H. pylon
diagnostic assay for active infection that was previously positive prior to
initiation of
treatment. If the diagnostic test for active gastrointestinal H. pylon
infection remains positive
after completion of first-line therapy, the possibility of antibiotic-
resistant H. pylon exists and
would entail additional treatment until a negative diagnostic test result was
obtained.
[0010] Next generation sequencing (NGS) can be used to gather massive
amounts of
data about the nucleic acid content of a sample. It can be particularly useful
for analyzing
nucleic acids in complex samples, such as clinical samples. However, before
using the NGS
methods, a starting sample must often be processed, which lowers nucleic acid
recovery,
delays sequencing, delays reporting of clinical calls, introduces errors,
introduces bias, and
often results in chemical waste requiring controlled handling. Errors and
biases can affect
results in many cases, such as when there are low abundance nucleic acids or
target nucleic
acids in patient samples. Current NGS methods focus on the abundance or
relative abundance
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of particular reads or sequences. Further many sequencing library preparation
methods and
some next generation sequencing systems yield experimentally observed target
nucleic acid
fragment lengths and fragment length distributions that are biased away from
the endogenous
fragment lengths and fragment length distributions, particularly such as those
methods and
systems that utilize variable polyA tail tagging, unaccounted polyA tail
tagging, thermal
deactivation of enzymes, use of biased extraction methods, or use other
measures that
introduce nucleic acid length, secondary structure, and/or GC biases within
the entire or
partial range of target nucleic acid lengths and GC-content. Some such methods
and systems
prevent a successful bias correction even in the presence of process control
molecules, if the
biases are so strong that insufficient target nucleic acid and/or process
control molecules are
recovered within the entire or certain section of the relevant lengths and GC-
contents for the
final analysis.
[0011] Various methods including NGS have been used to identify a microbe
present
in a host, but most of those methods have focused on the abundance of
microbial reads rather
than the physical properties of the molecules being read. For example many
extraction
protocols, library generation protocols, and sequencing protocols include
steps or processes
designed to remove short nucleic acid fragment lengths. Short nucleic acid
fragment lengths
are also often sacrificed in order to minimize undesirable or incomplete
byproducts of
extraction, library generation, or amplification, such as primer dimers or
adapter dimers.
Microbial cell-free nucleic acids are an example of target nucleic acids that
are particularly
vulnerable to biases and depletion of short nucleic acids as their fragment
lengths are below
about 100 bp.
[0012] The current approach for distinguishing between an invisible or
latent stage
infection and other stages of infection after identifying a potential
pathogen, can sometimes
call for an invasive biopsy procedure. Non-invasive tests, such as serology,
can detect
markers of exposure to microbes, but do not indicate if the infection is
active or at risk of
progressing to invasive disease. Thus, there is a need for accurate, non-
invasive approach for
determining if a patient's organ has been infected and distinguishing which
patients will
remain in colonization stage, and which are at risk to develop a secondary
invasive disease.
The present disclosure provides non-invasive methods, compositions, and kits
to detect an
infection in a subject and determine if the infection is at a colonization or
an invasive disease
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stage. The present disclosure also provides non-invasive methods to determine
the site of
localization in a subject and/or infection stage in a subject.
SUMMARY
[0013] An embodiment of the application provides a fragment length profile
from a
nucleic acid library wherein the nucleic acids used to prepare the nucleic
acid library were
obtained from a sample through an unbiased method, a method enabling bias
correction, or a
method with a reproducible bias. In various aspects the nucleic acid library
was generated
from an initial sample and the nucleic acids used to generate the nucleic acid
library are not
extracted from the initial sample before preparing the nucleic acid library or
before initiating
library generation process. Aspects of the methods may comprise nucleic acid
sequencing as
a step following the nucleic acid preparation and preceding determining the
fragment length
profile of target nucleic acids, multiple target nucleic acids or a subset of
nucleic acids within
a nucleic acid library. In aspects of the embodiment, the fragment length
profile comprises
one or more characteristics selected from the group comprising shape of the
distribution,
segment amplitude, segment fraction, peak shape, number of peaks, position of
a maximum of
a peak, the fragment count ratio for two or more segments, the height of
helical phasing
peaks, fragment count ratio at two different fragment lengths, ratio of
fragment counts within
two different fragment length ranges, the amount of fragments within a
segment, the fragment
length range within a segment, the ratio of maximum amplitudes for two or more
segments,
and fragment length distribution within a subset of reads, slope within a
segment, peak width,
the rate of count decay or increase within a segment, number of peaks, scaling
of the count
decay or increase within a segment.
[0014] Methods of generating a fragment length profile for a nucleic acid
library are
provided. The various methods comprise the steps of preparing a nucleic acid
library from an
initial sample using a bias-corrected recovery method, or a method with a
reproducible bias,
determining the number or normalized count of reads of multiple fragment
lengths within the
nucleic acid library, determining one or more fragment length characteristics
of the nucleic
acid library and generating a fragment length profile for the nucleic acid
library using one or
more fragment length characteristics. In aspects of the embodiment, the
fragment length
profile comprises one or more fragment length characteristics selected from
the group
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comprising shape of the distribution, segment amplitude, peak shape, the
fragment count ratio
for two or more segments, the height of helical phasing peaks, fragment count
ratio at two
different fragment lengths, ratio of fragment counts within two different
fragment length
ranges, the fragment length range within a segment, the ratio of maximum
amplitudes for two
or more segments, position of a maximum of a peak, number of peaks, and
fragment length
distribution within a subset of reads. Methods of generating a fragment length
profile for a
nucleic acid library are provided. The various methods comprise the steps of
preparing a
nucleic acid library from an initial sample comprising the steps of optionally
adding one or
more process control molecules to the initial sample to provide a spiked
initial sample and
generating a nucleic acid library from the spiked initial sample, wherein the
nucleic acids used
to generate the nucleic acid library are optionally not extracted from the
initial sample prior to
preparing the nucleic acid library. Aspects of the methods may comprise
nucleic acid
sequencing as a step following the nucleic acid preparation and preceding
determining the
fragment length profile. The methods of generating a fragment length profile
for target
nucleic acids within the nucleic acid library further comprise the steps of
determining the
number of reads of multiple fragment lengths within the nucleic acid library,
determining one
or more fragment length characteristics of the nucleic acid library and
generating a fragment
length profile for the nucleic acid library using one or more fragment length
characteristics. In
aspects of the embodiment, the fragment length profile comprises one or more
fragment
length characteristics selected from the group comprising shape of the
distribution, segment
amplitude, peak shape, number of peaks, position of a maximum of a peak, the
fragment
count ratio for two or more segments, the height of helical phasing peaks,
fragment count
ratio at two different fragment lengths, ratio of fragment counts within two
different fragment
length ranges, the fragment length range within a segment, the ratio of
maximum amplitudes
for two or more segments, and fragment length distribution within a subset of
reads. In
certain aspects, the step of generating the nucleic acid library from the
initial sample further
comprises, consists of, or consists essentially of the steps of
dephosphorylating nucleic acids
from the initial sample to produce a group of dephosphorylated nucleic acids,
denaturing the
dephosphorylated nucleic acids to produce denatured nucleic acids, attaching a
3'-end adapter
to the denatured nucleic acids to produce adapted nucleic acids, separating
adapted nucleic
acids, annealing a primer to the adapted nucleic acids and extending the
primer with a
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polymerase to generate complementary strands, attaching a 5'-end adapter,
eluting the strands
and amplifying the complementary strands. Aspects of the methods may comprise
nucleic
acid sequencing as a step following the nucleic acid preparation and preceding
determining
the fragment length profile. In various embodiments the number of reads is a
normalized
number of reads. In some embodiments the fragment length profile is for at
least one subset
of reads within the nucleic acid library. In such embodiments, the methods
further comprise
the steps of identifying at least one subset of reads within the nucleic acid
library and
determining the fragment length distribution within each selected subset of
reads. In some
embodiments the step of generating the fragment length profile further
comprises using two or
more fragment length characteristics.
[0015] Methods of identifying a microbe present in a sample are provided.
Methods
of identifying or characterizing a microbe present in a sample comprise the
steps of
generating a fragment length profile for the sequencing reads from a nucleic
acid library
generated from the sample and aligned to the microbe reference sequence,
comparing the
fragment length profile to reference fragment length profiles of one or more
microbes, and if
the fragment length profile from the sample is similar to a reference fragment
length profile of
a microbe, then identifying the microbe as present in the sample. Aspects of
the method
comprise comparing fragment length profiles for target sequences from a
nucleic acid library.
In various embodiments, the fragment length profile may indicate the microbe
is present as a
pathogen or a commensal microorganism. In aspects of the methods, generating a
fragment
length profile for the nucleic acid library comprises the steps of preparing a
nucleic acid
library from an initial sample, quantifying the number of reads of multiple
fragment lengths
within the nucleic acid library; determining one or more fragment length
characteristics of the
nucleic acid library or at least one subset of reads the nucleic acid library,
and generating a
fragment length profile for the nucleic acid library or at least one subset of
reads using one or
more fragment length characteristics. The step of preparing a nucleic acid
library from an
initial sample further comprises the steps adding one or more process control
molecules to the
initial sample to provide a spiked initial sample and generating a nucleic
acid library from the
spiked initial sample, wherein nucleic acids used to generate the nucleic acid
library are not
extracted from the initial sample before preparing the nucleic acid library.
Aspects of the
methods may comprise nucleic acid sequencing as a step following the nucleic
acid
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preparation and preceding determining the fragment length profile. In aspects
of the
embodiment, the fragment length profile comprises one or more fragment length
characteristics selected from the group comprising shape of the distribution,
segment
amplitude, peak shape, number of peaks, position of the maximum of the peak,
the fragment
count ratio for two or more segments, the height of helical phasing peaks,
fragment count
ratio at two different fragment lengths, ratio of fragment counts within two
different fragment
length ranges, the fragment length range within a segment, the ratio of
maximum amplitudes
for two or more segments, and fragment length distribution within a subset of
reads. In
various aspects of the methods, the fragment length profile comprises at least
one fragment
length characteristic selected from the group comprising fragment count ratio
for two or more
segments, peak shape, peak width, the rate of count decay or increase within a
segment,
number of peaks, scaling of the count decay or increase within a segment,
position of the
maximum of the peak.
[0016] Methods of determining the site of localization in a subject are
provided. The
methods comprise the steps of generating a fragment length profile for target
nucleic acids in
a nucleic acid library or the entire nucleic acid library generated from the
sample, comparing
the fragment length profile to a reference fragment length profile of one or
more source sites,
and if the fragment length profile from the sample is similar to a fragment
length profile from
a first source site, then predicting the first site as a site of localization,
if the fragment length
profile from the sample is similar to a fragment length profile from a second
source site, then
predicting the second site as a site of localization. In embodiments of the
methods, generating
one or more fragment length profile for the nucleic acid library comprises the
steps of
preparing a nucleic acid library from an initial sample, quantifying the
number of reads of
multiple fragment lengths within the nucleic acid library, generating a
fragment length profile
for target nucleic acids in a nucleic acid library or the entire nucleic acid
library nucleic acid
library using one or more fragment length characteristics. In embodiments of
the method,
preparing a nucleic acid library from an initial sample further comprises the
steps of adding
one or more process control molecules to the initial sample to provide a
spiked initial sample
and generating a nucleic acid library from the spiked initial sample, wherein
nucleic acids
used to generate the nucleic acid library are not extracted from the initial
sample before
preparing the nucleic acid library. Aspects of the methods may comprise
nucleic acid
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sequencing as a step following the nucleic acid preparation and preceding
determining the
fragment length profile. In aspects of the embodiment, the fragment length
profile comprises
one or more fragment length characteristics selected from the group comprising
shape of the
distribution, segment amplitude, peak shape, number of peaks, a position of
the maximum of
the peak, the fragment count ratio for two or more segments, the height of
helical phasing
peaks, fragment count ratio at two different fragment lengths, ratio of
fragment counts within
two different fragment length ranges, the fragment length range within a
segment, the ratio of
maximum amplitudes for two or more segments, peak width, the rate of count
decay or
increase within a segment, number of peaks, scaling of the count decay or
increase within a
segment, and fragment length distribution within a subset of reads. In aspects
of the methods,
the site of localization is selected from the group of source sites
comprising, consisting of, or
consisting essentially of deep tissue, lung, liver, bone, kidney, brain,
heart, sinus, GI tract,
spleen, skin, joint, ear, nose, mouth, bloodstream and blood.
[0017] Methods of monitoring transplant status in a subject are provided.
The
methods of monitoring transplant status comprise the steps of generating a
baseline fragment
length profile from a nucleic acid library from a sample obtained from the
subject, generating
a second fragment length profile for a nucleic acid library generated from a
second sample
obtained from the subject and comparing the second fragment length profile to
the baseline
fragment length profile. If the second fragment length profile differs from
the baseline
fragment length profile then internally administering an increased amount of
an anti-rejection
therapy to the subject, wherein a risk of rejection in a subject with a
transplant is lower
following the administration of the anti-rejection therapy. If the second
fragment length
profile is similar to the baseline fragment length profile, then maintaining
or reducing an anti-
rejection therapy, wherein the risk of a side-effect of the anti-rejection
therapy in the subject
is lower than it would be if the subject received an increased amount of the
anti-rejection
therapy. Aspects of the method comprise the step of comparing a fragment
length profile for
target nucleic acids in a nucleic acid library or the entire library from a
sample obtained from
a subject with a transplant and comparing the profile to a reference fragment
length profile.
[0018] Methods of monitoring toxicity of a compound administered to a
subject are
provided. The methods comprise the steps of generating a fragment length
profile for a
nucleic acid library or for target nucleic acids in the nucleic acid library
prepared from a
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sample obtained from the subject and comparing the fragment length profile to
one or more
reference fragment length profiles. In aspects of the method, the subject has
cancer, is at risk
for cancer or exhibits a cancer related symptom. In aspects of the method, the
one or more
reference fragment length profiles were generated from a nucleic acid library
obtained from a
subject or cell exposed to the compound. In aspects of the method, the one or
more reference
fragment length profiles comprises a baseline fragment length profile. In
aspects of the
method, the compound is a chemotherapeutic agent. In embodiments of the
method, the step
of generating a fragment length profile for a nucleic acid library comprises
the steps of
preparing a nucleic acid library from an initial sample using a bias-corrected
recovery
method; determining the number of reads of multiple fragment lengths within
the nucleic acid
library; determining one or more fragment length characteristics of the
nucleic acid library;
and generating a fragment length profile for the nucleic acid library using
one or more
fragment length characteristics. Aspects of the methods may comprise nucleic
acid
sequencing as a step following the nucleic acid preparation and preceding
determining the
fragment length profile. In aspects of the embodiment, the fragment length
profile comprises
one or more fragment length characteristics selected from the group comprising
shape of the
distribution, segment amplitude, peak shape, the fragment count ratio for two
or more
segments, the height of helical phasing peaks, fragment count ratio at two
different fragment
lengths, ratio of fragment counts within two different fragment length ranges,
the fragment
length range within a segment, the ratio of maximum amplitudes for two or more
segments,
and fragment length distribution within a subset of reads. In embodiments of
the methods,
generating a fragment length profile for the nucleic acid library comprises
the steps of
preparing a nucleic acid library from an initial sample further comprising
adding one or more
process control molecules to the initial sample to provide a spiked initial
sample and
generating a nucleic acid library from the spiked initial sample, wherein
nucleic acids used to
generate the nucleic acid library are not extracted from the initial sample
before preparing the
nucleic acid library; quantifying the number of reads of multiple fragment
lengths within the
nucleic acid library; determining one or more fragment length characteristics
of the nucleic
acid library; and generating a fragment length profile for the nucleic acid
library using one or
more fragment length characteristics. In aspects of the embodiment, the
fragment length
profile comprises one or more fragment length characteristics selected from
the group
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comprising shape of the distribution, segment amplitude, peak shape, the
fragment count ratio
for two or more segments, the height of helical phasing peaks, fragment count
ratio at two
different fragment lengths, ratio of fragment counts within two different
fragment length
ranges, the fragment length range within a segment, the ratio of maximum
amplitudes for two
or more segments, and fragment length distribution within a subset of reads.
[0019] The present invention is directed to methods to predict the risk
that an
organism (or multiple organisms) present in a host create a localized or
systemic
environmental change or invasion of organs or anatomical systems with
substantially negative
health outcomes. An organism is invasive if it passes a barrier or
translocates from one organ
or anatomical structure to another, invades a structure beyond the tissue
layer it occupied in a
colonizing state to create a localized invasion, it changes the environment of
a structure such
that it creates significant negative impacts to the structure or causes DNA
mutations or
inflammation, or it otherwise overwhelms the host's immune system.
[0020] In certain embodiments, the risk level is based on the abundance of
the
organism in the host as compared to an asymptomatic control or infected
control. In other
embodiments, the abundance is a threshold or range. In yet other embodiments,
the risk level
is calculated as a clinical decision-making score based on one or more of the
following:
abundance of the organism, clinical history of the patient, chronicity of
disease, genetic
biomarker factors and patient characteristics (such as age, gender, etc.),
fragment length
distribution profile, and a fragment length distribution profile
characteristic.
[0021] In an aspect there is provided a method to determine the infection
stage of a
subject suspected of having a microbial infection comprising:
(a) performing high throughput sequencing of nucleic acids from said
biological sample;
(b) performing bioinformatics analysis to identify microbial nucleic acid
sequences present in said biological sample; and
(c) calculating a measurement for the nucleic acids and comparing the
measurement to a control, thereby determining the infection stage for any
microbe identified
in said biological sample.
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[0022] In some embodiments the method further comprises one or more steps
selected
from the group consisting of (a) extracting nucleic acids from a portion of a
biological sample
obtained from the subject and (b) adding synthetic nucleic acid spike-ins.
[0023] In one embodiment, the measurement of step (c) is selected from an
absolute
abundance for the cell-free microbial nucleic acid sequences, a distribution
of fragment
lengths for the nucleic acids sequences, a characteristic of the nucleic acid
fragment length
distribution profile, or a combination thereof. In another embodiment, the
measurement of
step (c) is an absolute abundance and distribution of fragment lengths for the
target pathogen.
[0024] In a second embodiment, the subject has symptoms of an infection or
is at risk of
having an infection.
[0025] In a third embodiment, the infection stage is an invisible phase, a
symptomatic
phase of an infection, a treatment phase or an eradication stage. In a fourth
embodiment, the
method further comprises repeating the method over time to monitor an
infection, stage of
infection, efficacy of a treatment for an infection, or detect the onset of an
infection. In
aspects, the methods may further comprise changing a therapeutic regimen.
[0026] In a fifth embodiment, the method further comprises administering a
therapeutic
regimen to the subject based on the determined infection stage.
[0027] In a sixth embodiment, the high-throughput sequencing assay is next
generation
sequencing, massively-parallel sequencing, pyrosequencing, sequencing-by-
synthesis, single
molecule real-time sequencing, polony sequencing, DNA nanoball sequencing,
heliscope
single molecule sequencing, nanopore sequencing, Sanger sequencing, shotgun
sequencing, or
Gilbert's sequencing.
[0028] In a seventh embodiment, the sample is blood, plasma, serum,
cerebrospinal
fluid, synovial fluid, bronchial-alveolar lavage, sputum, urine, stool,
saliva, or a nasal sample.
[0029] In an eighth embodiment, method further comprises identifying
antibiotic-
resistant gene(s) of the target pathogen.
[0030] In a ninth embodiment, method further comprises identifying at least
one risk
factor in the subject's genomic DNA.
[0031] In a tenth embodiment, the nucleic acid is cell-free DNA and/or cell-
free RNA.
The nucleic acids may comprise cell-free pathogen DNA. The nucleic acids may
comprise
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cell-free pathogen RNA. The nucleic acids may comprise cell-free microbial
DNA. The
nucleic acids may comprise cell-free microbial RNA.
[0032] In an eleventh embodiment, the target pathogen is Heliobacter
pylori,
Clostridium difficile, Haemophilus influenza, Salmonella, Streptococcus
pneumoniae,
Cytomegalovirus, hepatitis virus B, hepatitis virus C, human papillomavirus,
Epstein¨Ban
virus, human T-cell lymphoma virus 1, Merkel cell polyomavirus, Kaposi's
sarcoma virus,
Human Herpesvirus 8, Chlamydia, Gonorrhea, Syphilis, or Trichomoniasis.
[0033] In a twelfth embodiment, the subject previously had another test or
other clinical
tests. In an embodiment, the other clinical test is stool antigen test, urea
breath test, serology,
urease testing, histology, bacterial culture and sensitivity testing, biopsy,
or endoscopy.
[0034] In a thirteenth embodiment, the target pathogen nucleic acids is DNA
and/or RNA.
The pathogen nucleic acids comprise cell-free DNA. The nucleic acids comprise
pathogen
cell-free RNA.
[0035] In a fourteenth embodiment, synthetic nucleic acid spike-ins
comprises at least
1000 unique synthetic nucleic acids to the sample, wherein each of the 1000
unique synthetic
nucleic acids comprises (i) an identifying tag and (ii) a variable region
comprising at least 5
degenerate bases. In a further embodiment, the method further comprises
(a) optionally extracting the nucleic acids from the spiked-sample;
(b) generating a spiked-sample library;
(c) optionally enriching the spiked-sample library;
(d) conducting a high-throughput sequencing assay to obtain sequence reads
from the spiked-sample library;
(e) calculating a diversity loss value of the 1,000 unique synthetic nucleic
acids
and;
(f) calculating a measurement for the nucleic acids and comparing the
measurement to a control, thereby determining the infection stage in the
subject.
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[0036] In a yet further embodiment, the at least 1,000 unique synthetic
nucleic acids
are synthetic nucleic acids as described in U.S. 9,976,181.
[0037] In another aspect there is a method of determining the infection
stage of
Heliobacter pylori in a subject comprising:
a) optionally, extracting cell-free nucleic acids from a biological sample
obtained from said subject;
b) adding synthetic nucleic acid spike-ins to the sample;
c) performing high throughput sequencing of nucleic acids from said
biological sample;
d) performing bioinformatics analysis to identify Heliobacter pylori
nucleic acid sequences present in said biological sample; and
e) calculating a measurement for the Heliobacter pylon nucleic acids and
comparing the measurement to a control, thereby determining the infection
stage for
Heliobacter pylon in said subject.
[0038] In a first embodiment, the measurement is an absolute abundance or
a
distribution of fragment lengths or combination thereof for Heliobacter pylon.
In an
embodiment, the measurement is an absolute abundance for Heliobacter pylon. In
another
embodiment, the measurement is a distribution of fragment lengths for
Heliobacter pylon. In
yet another embodiment, the measurement is an absolute abundance and
distribution of
fragment lengths for Heliobacter pylon. In various embodiments, the steps of
the method
may be carried out in varying order.
[0039] In a second embodiment, the subject has symptoms of a Heliobacter
pylori
infection or is at risk of having a Heliobacter pylon infection. In an
embodiment, the
infection stage is an invisible phase, a symptomatic phase of an infection, a
treatment phase or
an eradication stage.
[0040] In a third embodiment, the method further comprises repeating the
method
over time to monitor an infection or efficacy of a treatment for an infection.
[0041] In an aspect there is a method of determining the infection stage
of Heliobacter
pylon in subject comprising:
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(a) making a spiked-sample by obtaining a sample from a subject
comprising cell-free nucleic acids and adding one or more process control
molecules;
(b) optionally, extracting the nucleic acids from the spiked-sample;
(c) generating a spiked-sample library, wherein the generating comprises
(i) attaching an adapter to nucleic acids and (ii) amplifying;
(d) optionally, enriching the spiked-sample library;
(e) conducting a high-throughput sequencing assay to obtain sequence
reads from the spiked-sample library;
(0 calculating a diversity loss value of the 1,000 unique
synthetic nucleic
acids and;
(g) calculating a measurement for the cell-free nucleic acids and
comparing
the measurement to a control, thereby determining the infection stage of
Heliobacter pylon in
the subject.
[0042] In a yet further embodiment, the at least 1,000 unique synthetic
nucleic acids are
synthetic nucleic acids as described in U.S. 9,976,181.
[0043] In a second embodiment, the high-throughput sequencing assay is next
generation sequencing, massively-parallel sequencing, pyrosequencing,
sequencing-by-
synthesis, single molecule real-time sequencing, polony sequencing, DNA
nanoball
sequencing, heliscope single molecule sequencing, nanopore sequencing, Sanger
sequencing,
shotgun sequencing, or Gilbert's sequencing.
[0044] In a third embodiment, the sample is blood, plasma, serum,
cerebrospinal fluid,
synovial fluid, bronchial-alveolar lavage, urine, stool, saliva, or a nasal
sample.
[0045] In a fourth embodiment, the method further comprises administering a
therapeutic regimen to the subject, wherein the treatment can be administered
at any stage of
the infection cycle.
[0046] In a fifth embodiment, the method further comprises identifying
antibiotic-
resistant gene(s) of the target pathogen.
[0047] In a sixth embodiment, the cell-free nucleic acid is DNA and/or RNA.
The
nucleic acids comprise cell-free pathogen DNA. The nucleic acids comprise cell-
free
pathogen RNA.
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[0048] In a seventh embodiment, the subject previously had another other
clinical test.
In an embodiment, the other clinical test is stool antigen test, urea breath
test, serology, urease
testing, histology, bacterial culture and sensitivity testing, biopsy, or
endoscopy.
[0049] In an eighth embodiment, the target pathogen nucleic acids is DNA
and/or RNA.
The pathogen nucleic acids comprise cell-free DNA. The nucleic acids comprise
pathogen
cell-free RNA. The target pathogen nucleic acids comprise a mixture of cell-
free DNA and
cell-free RNA.
[0050] Another aspect provides a method of determining a site of
localization in a
subject infected by a pathogen comprising:
(a) obtaining a sample from a subject comprising nucleic acids and adding
one or more process control molecules, thereby generating a spiked sample;
(b) optionally, extracting the nucleic acids from the spiked-sample;
(c) generating a library from the spiked-sample, where in generating
comprises attaching an adapter to nucleic acids and amplifying;
(d) optionally, enriching the spiked-sample;
(e) conducting a high-throughput sequencing assay to obtain sequence
reads from the spiked-sample by comparing to a reference genome;
(0 optionally, calculating a diversity loss value and;
(g) calculating a measurement for the nucleic acids and comparing
the
measurement to a control, thereby determining a site of localization in the
subject.
[0051] In a first embodiment, the measurement is an absolute abundance or a
distribution of fragment lengths or combination thereof for a target pathogen.
In an
embodiment, the measurement is an absolute abundance for a target pathogen. In
another
embodiment, the measurement is a distribution of fragment lengths for a target
pathogen. In
yet another embodiment, the measurement is an absolute abundance and
distribution of
fragment lengths for a target pathogen.
[0052] In a second embodiment, the site of localization is a tissue. In a
further
embodiment, the site of localization is a tissue type. In a yet further
embodiment, the site of
localization is an organ. In another further embodiment, the site of
localization is a tissue
type comprising an organ.
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[0053] In a third embodiment, the subject has symptoms of an infection or
at risk of
having an infection. In a further embodiment, the subject has been previously
identified as
being infected with Heliobacter pylori, Clostridium difficile, Haemophilus
influenza,
Salmonella, Streptococcus pneumoniae, Cytomegalovirus, Hepatitis B Virus,
Hepatitis C
Virus, Human papillomavirus, Epstein¨Barr virus, Human T-cell lymphoma virus
1, Merkel
cell polyomavirus, Kaposi's sarcoma virus, Human Herpesvirus 8, Chlamydia,
Herpes
Simplex Virus, Neisseria species, Treponema species, or Trichomonas species.
[0054] In a fourth embodiment, the method is repeated over time to monitor
an infection
or efficacy of a treatment for an infection.
[0055] In a fifth embodiment, the method further comprises administering a
therapeutic
regimen to the subject based on the determined infection stage.
[0056] In a sixth embodiment, the at least 1,000 unique synthetic nucleic
acids are
synthetic nucleic acids as described in U.S. 9,976,181.
[0057] In a seventh embodiment, the high-throughput sequencing assay is
next
generation sequencing, massively-parallel sequencing, pyrosequencing,
sequencing-by-
synthesis, single molecule real-time sequencing, polony sequencing, DNA
nanoball
sequencing, heliscope single molecule sequencing, nanopore sequencing, Sanger
sequencing,
shotgun sequencing, or Gilbert's sequencing.
[0058] In an eighth embodiment, the sample is blood, plasma, serum,
cerebrospinal
fluid, synovial fluid, bronchial-alveolar lavage, urine, stool, saliva, nasal,
or tissue sample.
[0059] In a ninth embodiment, the method further comprises identifying
antibiotic-
resistant gene(s) of the pathogen.
[0060] In a tenth embodiment, the method further comprises identifying risk
factor in
the subject's genomic DNA.
[0061] In an eleventh embodiment, the target pathogen nucleic acids is DNA
and/or
RNA. The pathogen nucleic acids comprise cell-free DNA. The nucleic acids
comprise
pathogen cell-free RNA. The target pathogen nucleic acids comprise a mixture
of cell-free
DNA and cell-free RNA.
[0062] In a twelfth embodiment, the cell-free nucleic acid is DNA and/or
RNA. The
nucleic acids comprise cell-free pathogen DNA. The nucleic acids comprise cell-
free RNA.
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The nucleic acids comprise cell-free pathogen RNA. The nucleic acids comprise
cell-free
subject RNA. The nucleic acids comprise pathogen and subject cell-free RNA.
[0063] In an aspect, there is provided a method to determine the infection
stage of a
subject suspected of having a microbial infection comprising
(a) Providing a sample from said subject comprising nucleic acids
(b) Adding at least 1000 unique synthetic nucleic acids to the sample,
thereby generating a spiked-sample;
(c) generating a library from the spiked-sample;
(d) conducting a high-throughput sequencing assay to obtain sequence
reads from the spiked-sample;
(e) determining the infection stage of said subject based upon the sequence
reads.
[0064] In an embodiment, the sample is selected from blood, plasma, serum,
cerebrospinal fluid, synovial fluid, bronchial-alveolar lavage, urine, stool,
saliva, nasal, and
tissue sample. The sample is a blood, plasma, serum, cerebrospinal fluid, or
synovial fluid.
[0065] In a yet further embodiment, the at least 1,000 unique synthetic
nucleic acids
are synthetic nucleic acids as described in U.S. 9,976,181.
[0066] In a further embodiment, the high-throughput sequencing assay is
next
generation sequencing, massively-parallel sequencing, pyrosequencing,
sequencing-by-
synthesis, single molecule real-time sequencing, polony sequencing, DNA
nanoball
sequencing, heliscope single molecule sequencing, nanopore sequencing, Sanger
sequencing,
shotgun sequencing, or Gilbert's sequencing.
[0067] In another further embodiment, the determination of the infection
stage is
based on an absolute abundance or a fragment length distribution profile or
combination
thereof for a target pathogen. In an embodiment, the determination based on an
absolute
abundance for a target pathogen. In another embodiment, the determination
based on a
distribution of fragment lengths for a target pathogen. In yet another
embodiment, the
determination based on an absolute abundance and distribution of fragment
lengths for a
target pathogen.
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[0068] An aspect of the application provides a method of determining
infection stage
in a subject. The method comprises the steps of generating a fragment length
profile for a
nucleic acid library generated from a sample obtained from said subject,
comparing the
fragment length profile to a reference fragment length profile, and if the
fragment length
profile from the sample is similar to a fragment length profile from a
symptomatic subject,
then determining the infection stage indicates the subject has an increased
risk of exhibiting a
microbe related symptom and if the fragment length profile from the sample is
similar to a
fragment length profile from an asymptomatic subject, then determining the
infection in the
invisible stage. In an aspect, the fragment length profile is a non-microbial
host nucleic acid
library fragment length profile. In various aspects, the method further
comprises the steps of
determining an abundance of at least one significant microbe in a sample from
the subject,
comparing the abundance to a threshold and comparing the fragment length
profile to a
reference fragment length profile. If the fragment length profile from the
sample is similar to
a fragment length profile from a symptomatic subject and said abundance is
comparable to or
above a threshold, then determining the infection stage indicates the subject
has an increased
risk of exhibiting a microbe related symptom. If the fragment length profile
from the sample
is similar to a fragment length profile from an asymptomatic subject, then
determining the
infection is in the invisible stage. In an aspect, the method further
comprises the step of
administering an anti-microbial agent to a subject determined to have an
increased risk of
exhibiting a microbe-related symptom.
[0069] A method of determining the infection stage of a subject suspected
of having a
microbial infection comprising performing high-throughput sequencing of
nucleic acids from
the biological sample, performing bioinformatics analysis to identify nucleic
acid sequences
present in the biological sample and calculating a measurement for the nucleic
acids and
comparing the measurement to a control thereby determining the infection stage
for a microbe
identified in the biological sample. The method may further comprise one or
more steps
selected from the group consisting of (i) extracting nucleic acids from a
biological sample
obtained from the subject and (ii) adding synthetic nucleic acid spike-ins to
biological sample
obtained from the subject. In an aspect, the nucleic acids comprise microbial
nucleic acids,
host nucleic acid or both microbial and host nucleic acids. In an aspect, the
nucleic acids
comprise cell-free microbial nucleic acids, host nucleic acid or both
microbial and host
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nucleic acids. In an aspect the measurement is selected from the group of
measurements
consisting of an absolute abundance for the nucleic acids, a fragment length
distribution
profile for the nucleic acids and both an absolute abundance and a fragment
length
distribution profile. In an aspect, the infection stage is selected from an
invisible stage of
infection, colonization stage, symptomatic stage, active stage, invasive
disease stage,
resolution stage, treatment phase or an eradication stage. In an aspect, the
method further
comprises administering a therapeutic regimen to a subject based on the
determined infection
stage. The method may further comprise repeating the method over time to
monitor the
infection or efficacy of a treatment for the infection. In some aspects, the
microbe is selected
from the group comprising Heliobacter pylori, Clostridium difficile,
Haemophilus influenza,
Salmonella, Streptococcus pneumoniae, Cytomegalovirus, Hepatitis B Virus,
Hepatitis C
Virus, Human papillomavirus, Epstein¨Barr virus, Human T-cell lymphoma virus
1, Merkel
cell polyomavirus, Kaposi's sarcoma virus, Human Herpesvirus 8, Chlamydia,
Herpes
Simplex Virus, Neisseria species, Treponema species, or Trichomonas species.
In aspects
adding the synthetic nucleic acid spike-ins further comprises making a spiked-
sample by
obtaining a sample from a subject comprising cell-free nucleic acids and
adding one or more
process control molecules; extracting the nucleic acids from the spiked-
sample; generating a
spiked-sample library; enriching the spiked-sample library; conducting a high-
throughput
sequencing assay to obtain sequence reads from the spiked-sample library;
calculating a
diversity loss value of the 1,000 unique synthetic nucleic acids and;
calculating a
measurement for the cell-free nucleic acids and comparing the measurement to a
control,
thereby determining the infection stage in the subject.
[0070] In an embodiment, the application provides a method of determining
the
infection stage of Heliobacter pylori in a subject comprising extracting
nucleic acids from a
biological sample obtained from the subject, adding synthetic nucleic acid
spike-ins to the
sample, performing high throughput sequencing of nucleic acids from the
biological sample,
performing bioinformatic analysis to identify cell-free Heliobacter pylon
nucleic acid
sequences present in a biological sample and calculating a measurement for the
cell-free
Heliobacter pylon nucleic acids and comparing the measurement to a control,
thereby
determining the infection stage for Heliobacter pylon in the subject.
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[0071] In an embodiment the application provides a method of determining
the
infection stage of Heliobacter pylori in subject comprising: making a spiked-
sample by
obtaining a sample from a subject comprising cell-free nucleic acids and
adding one or more
process control molecules ; extracting the nucleic acids from the spiked-
sample; generating a
spiked-sample library, wherein the generating comprises (i) attaching an
adapter to nucleic
acids and (ii) amplifying; optionally, enriching the spiked-sample library;
conducting a high-
throughput sequencing assay to obtain sequence reads from the spiked-sample
library;
calculating a diversity loss value of the 1,000 unique synthetic nucleic acids
and calculating a
measurement for the cell-free nucleic acids and comparing the measurement to a
control,
thereby determining the infection stage of Heliobacter pylori in the subject.
[0072] An embodiment provides methods of determining a site of
localization in a
subject infected by a pathogen comprising obtaining a sample from a subject
comprising
nucleic acids, adding one or more process control molecules to the initial
sample to provide a
spiked sample, optionally extracting the nucleic acids from the spiked sample,
generating a
library from the spiked sample, wherein generating comprises attaching an
adapter to said
nucleic acids and amplifying; optionally, enriching the spiked sample,
conducting a high-
throughput sequencing assay to obtain sequence reads from the spiked sample by
comparing
to a reference genome; determining one or more fragment length characteristics
of the nucleic
acid library, generating a fragment length profile for a nucleic acid library
generated from the
sample, comparing the fragment length profile to a reference fragment length
profile of one or
more source sites and if the fragment length profile from the sample is
similar to a fragment
length profile from a first source site, then identifying the first site as a
site of localization; if
the fragment length profile from the sample is similar to a fragment length
profile from a
second source site, then identifying the second site as a site of
localization.
[0073] An aspect provides a method of determining a site of localization
in a subject
infected by a pathogen comprising obtaining a sample from a subject comprising
cell-free
nucleic acids and adding one or more process control molecules , thereby
generating a spiked-
sample; optionally extracting the nucleic acids from the spiked-sample;
generating a library
from the spiked-sample, wherein generating comprises attaching an adapter to
said nucleic
acids and amplifying; optionally, enriching the spiked sample; conducting a
high-throughput
sequencing assay to obtain sequencing reads from the spiked-sample by
comparing to a
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reference genome; calculating a diversity loss value of the 1000 unique
synthetic nucleic acids
and calculating a measurement for the cell-free nucleic acids and comparing
the measurement
to a control, thereby determining the site of localization in the subject.
INCORPORATION BY REFERENCE
[0074] All publications, patents, and patent applications mentioned in
this
specification are herein incorporated by reference in their entireties to the
same extent as if
each individual publication, patent, or patent application was specifically
and individually
indicated to be incorporated by reference.
BRIEF DESCRIPTION OF THE DRAWINGS
[0075] The novel features of the invention are set forth with
particularity in the
appended claims. A better understanding of the features and advantages of the
present
invention will be obtained by reference to the following detailed description
that sets forth
illustrative embodiments, in which the principles of the invention are
utilized, and the
accompanying drawings of which:
[0076] FIG. 1 depicts one of the methods of this disclosure.
[0077] FIG. 2 depicts one of the cell-free methods of this disclosure.
[0078] FIG. 3 shows a schematic of an exemplary infection.
[0079] FIG. 4 depicts one of the infection site detection methods of this
disclosure.
[0080] FIG. 5 depicts a general scheme of a method for determining
diversity loss
value.
[0081] FIG. 6 shows a diagnostic workflow ending with treatment for a
positive
diagnosis of H. pylon.
[0082] FIG. 7 depicts a computer control system that is programmed or
otherwise
configured to implement methods provided herein.
[0083] FIG. 8 depicts the distribution of fragment lengths for reads from
three
microbes detected in three different human plasma samples from which nucleic
acid libraries
were generated. The fragment length characteristic of interest in the figure
is distribution
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shape. Each panel provides an example of a different distribution shape. In
each panel, the
normalized number of reads is shown on the y-axis and the fragment length is
indicated on x-
axis. The left panel provides an example of a "50-base pair peak" distribution
shape. The
middle panel provides an example of a short exponential-like distribution
shape. The right
panel provides an example of a complex distribution shape, wherein this
particular complex
distribution shape comprises aspects of the exponential decay like
distribution shape and a
single peak 50 base pair distribution. It is recognized that each distribution
shape depicted
reflect the distribution of fragment lengths in a nucleic acid library each
generated from a
distinct human plasma sample and provide one example of the indicated
distribution shape
type. Other distribution shapes are described elsewhere herein. Other
distribution shapes are
possible.
[0084] FIG. 9
provides examples relating to the fragment length characteristics of
distribution segment amplitude and segment amplitude ratios. The panels depict
the
distribution of fragment lengths for reads of the same pathogen (Candida
tropicalis) from
three different clinical samples. In each panel, the normalized number of
reads is shown on
the y-axis and the fragment length is indicated on x-axis. The clinical
samples are numbered 1
through 3 for the purpose of this figure. Candida tropicalis in Clinical
Samples 1 and 2 show
a distribution with higher long (> 65 bp) fraction relative to the 50 bp peak
as compared to
Candida tropicalis in Clinical Samples 3 while all fragment length profiles
have a clear peak
at approximately 45-50 bp. The ratio of short reads (<40 bp) relative to the
50 bp peak also
varies between the three samples. The distribution segment amplitude and
segment amplitude
ratios (<40 bp to 50 bp peak and >65 bp to 50 bp peak) reflect results
obtained from one
experiment.
[0085] FIG. 10
depicts fragment length distribution of WU polyomavirus from two
clinical samples. The left panel shows a distribution with a single peak
around the 50 base
pair (bp) fragment length. The right panel shows combination pattern
comprising an
exponentially distributed shape contribution, a peak, and a long fraction
contribution.
Without being limited by mechanism, the short-exponential like fraction may
suggest
incorporation of the virus in the human genome or degradation of the microbial
nucleic acids
by a process distinct from the one generating the fragments within the "50 bp
peak".
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[0086] FIG. 11 provides examples relating to the fragment length
characteristics of
fragment count ratio in different distributions. The panels depict the ratio
of fragment counts
in the "50 bp peak" fraction vs. short exponential-like fraction (read density
40-55 bp/read
density 23-35 bp, x-axis) versus normalized counts (y-axis). The same fraction
for human and
human mitochondria were added for reference. The ratio varies between kingdom
types. The
ratios for bacterial reads vary widely while the ratios for fungal reads show
a bimodal pattern.
The ratios for viral reads are also shown.
[0087] FIG. 12 provides a summary of fragment length distributions for
maternal
(dashed line) and fetal (solid line) cell-free nucleic acids. The "50 bp peak"
appears narrower
in the fetal distribution indicating a smaller fragment length range within
the peak from the
fetal nucleic acids. In addition, the ratio of fetal to maternal reads is
higher in the "50 bp
peak" region as compared to the nucleosomal length fragments (e.g. 150-200 bp
region).
[0088] FIG. 13 provides a summary of fragment length distribution for
microbes
present as a pathogen or as a commensal microorganism. Pathogens tend to have
longer
fragment lengths than commensal microorganisms in an end-repairable double-
stranded
DNA-based assay.
[0089] FIG. 14 provides a summary of fragment length distribution of
pathogens in
nucleic acid libraries generated from samples where infection was confirmed
either by urine
or blood cultures. Pathogens detected in nucleic acid libraries from samples
with orthogonal
blood culture tests show a higher fraction long reads than pathogens detected
in nucleic acid
libraries from samples with an orthogonal urine cultures. Read length is shown
on the x-axis;
fraction of reads is shown on the y-axis. The average of the urine culture
samples (light solid
line) and the average of the blood culture samples (light dashed line) are
shown on the graph,
as is the difference between urine and blood (bold dash dots).
[0090] FIG. 15 summarizes data obtained from asymptomatic samples (AP),
diagnostic positive samples (DP), diagnostic positive samples confirmed with
any orthogonal
method (DP) and diagnostic positive samples confirmed with an orthogonal NGS
method
(DPNos) and diagnostic positive samples confirmed with an orthogonal non-NGS
microbiological method (DP
micro) micro) as indicated. FIG. 15A provides plots of abundance in units
of molecules per microliter (MPM) for the microbes found at significant levels
in the
indicated sample type. FIG. 15B provides plots of MPM abundances in
asymptomatic
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samples (AP) and diagnostic positive samples (DP) for the microbes of the same
species
present in both types of samples. FIG. 15C provides an example of a
representative
TapeStation electropherogram of a library obtained from a diagnostic positive
sample
included in this study. The data was obtained on TapeStation using a HS
TapeStation tape
D1000 with the Loading Buffer and DNA Ladder according to the manufacturer's
instructions. The Upper and Lower DNA markers are indicated in the plot. A
subset of
regions of interests in the fragment length ranges are indicated in the plot
for orientation (note
that the fragment lengths in an electropherogram of a library reflect the
lengths of fully
adapted nucleic acid molecules rather than the actual lengths of the
endogenous originals).
Library fragment length is shown on the x-axis; normalized intensity (FU) is
shown on the y-
axis. FIG. 15D provides plots of the molar fractions of the sequencing reads
mapping to
human reference and longer than 64 bp (i.e. the majority of these reads are of
nucleosomal
length) after the adapter sequence trimming step for asymptomatic samples (AP)
and
diagnostic positive samples (DP) included in this study. FIG. 15E provides a
summary
comparison of the maximum MPM abundance for the microbes found at significant
levels in
each asymptomatic (AP) and diagnostic positive (DP) sample in this study with
the fraction of
the long human reads as defined in the caption to FIG. 15D and found in the
same samples.
Only AP and DP samples where our assay detected microbes at the significant
levels were
included in this analysis. Arrows indicate the AP samples that showed maximum
MPMs and
long human read fraction higher than 3000, and 0.4, respectively. FIG. 15F
provides a
summary comparison of the maximum MPM abundances for the microbes found at
significant levels in asymptomatic samples (AP) and diagnostic negative
samples (DN), with
the fraction of the long human reads as defined for FIG. 15D and found in the
same samples.
Only AP and DN samples where our assay detected microbes at the significant
levels were
included in this analysis.
[0091] FIG. 16A depicts the results of training a predictor of an
infection state based
on the human fragments recovered for sequencing from asymptomatic and
symptomatic
patients. The left panel shows probabilities for a sample to be asymptomatic
based on human-
trained model. The right panel depicts the regions of the fragment lengths
relevant to each
infection state used by the human-trained model. FIG. 16B depicts the results
of training a
predictor of an infection state based on the human mitochondrial fragments
recovered for
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sequencing from asymptomatic and symptomatic patients. The left panel shows
probabilities
for a sample to be asymptomatic based on human mitochondria-trained model. The
right
panel depicts the regions of the fragment lengths relevant to each infection
state used by the
human mitochondria-trained model. FIG. 16C depicts the results of training a
predictor of an
infection state based on the all pathogen fragments recovered for sequencing
from
asymptomatic and symptomatic patients. The left panel shows probabilities for
a sample to be
asymptomatic based on all pathogen fragment-trained model. The right panel
depicts the
regions of the fragment lengths relevant to each infection state used by all
pathogen fragment-
trained model. FIG. 16D depicts the results of training a predictor of an
infection state based
on the significant pathogen fragments recovered for sequencing from
asymptomatic and
symptomatic patients. The left panel shows probabilities for a sample to be
asymptomatic
based the model trained only on the reads derived from the significant
pathogens. The right
panel depicts the regions of the fragment lengths relevant to each infection
state recognized
by model trained on the significant pathogens. FIG. 16E depicts the results of
training a
predictor of an infection state based on the bacterial fragments recovered for
sequencing from
asymptomatic and symptomatic patients. The left panel shows probabilities for
a sample to be
asymptomatic based on bacteria-trained model. The right panel depicts the
regions of the
fragment lengths relevant to each infection state recognized by the bacteria-
trained model.
FIG. 16F depicts the results of training a predictor of an infection state
based on the
eukaryotic microbial fragments recovered for sequencing from asymptomatic and
symptomatic patients. The left panel shows probabilities for a sample to be
asymptomatic
based on eukaryota-trained model. The right panel depicts the regions of the
fragment lengths
relevant to each infection state recognized by the eukaryota-trained model.
FIG. 16G depicts
the results of training a predictor of an infection state based on the viral
fragments recovered
for sequencing from asymptomatic and symptomatic patients. The left panel
shows
probabilities for a sample to be asymptomatic based on virus-trained model.
The right panel
depicts the regions of the fragment lengths relevant to each infection state
recognized by the
virus-trained model. FIG. 16H depicts the results of training a predictor of
an infection state
based on the archaea fragments recovered for sequencing from asymptomatic and
symptomatic patients. The left panel shows probabilities for a sample to be
asymptomatic
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based on archaea-trained model. The right panel depicts the regions of the
fragment lengths
relevant to each infection state recognized by the archaea-trained model.
[0092] FIG. 17A The normalized fragment length distributions for the
microbes
suspected to be infecting lungs are shown with each panel showing one
distribution for the
indicated species of microbe and a Sample ID indicated at the top of each
panel. The
frequency is defined as the count of the reads aligning to the reference of
the indicated
microbe of a particular read (fragment) length normalized by the total count
of the reads
aligning to the reference of the indicated microbe. FIG. 17B The normalized
fragment length
distributions for the microbes suspected of infecting the bloodstream are
shown with each
panel showing one distribution for the indicated species of microbe and a
Sample ID indicated
at the top of each panel. The frequency is defined as the count of the reads
aligning to the
reference of the indicated microbe of a particular read (fragment) length
normalized by the
total count of the reads aligning to the reference of the indicated microbe.
[0093] FIG. 18A depicts representative normalized fragment length
distribution for
two microbes detected in the venous draws of two different donors. The
normalized fragment
length distribution of the reads mapping to Haemophilus influenzae, a microbe
detected in the
plasma obtained from the venous blood draw of Donor 1 is shown in the left
panel. The
normalized fragment length distribution of the reads mapping to Streptococcus
thermophilus,
a microbe detected in the plasma obtained from the venous blood draw of Donor
2 is shown in
the right panel. FIG. 18B depicts normalized fragment length distributions for
the microbes
detected in the biological samples obtained during the capillary draw
collection process from
the same two donors and drawn at the same sampling time as the venous draws in
Fig. 18A.
The upper left panel shows the normalized fragment length distribution of
Haemophilus
influenzae as detected in the biological sample obtained during the capillary
draw collection
process from Donor 1. The lower left panel shows the normalized fragment
length
distributions for the additional microbes detected in the biological sample
obtained during the
capillary draw collection process from Donor 1. Their mean distribution
pattern is shown in
bold black line. The upper right panel shows the normalized fragment length
distribution of
Streptococcus thermophilus as detected in the biological sample obtained
during the capillary
draw collection process from Donor 2. The lower right panel shows the
normalized fragment
length distributions for the additional microbes detected in the biological
sample obtained
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during the capillary draw collection process from Donor 2. Their mean
distribution pattern is
shown in bold black line. FIG. 18C compares the abundances for the co-
occurring microbes
in the two replicates of the biological sample obtained during the capillary
draw collection
process for Donor 1 (left panel) and Donor 2 (right panel). FIG. 18D depicts a
comparison of
the microbial abundances for the microbes detected in the biological sample
obtained with a
capillary blood draw procedure (x-axis) to the microbial abundance in the
Negative
Microvette Samples. The results obtained for Donor 1, and Donor 2 are shown in
the left and
right panel, respectively.
[0094] FIG. 19A Subject RD-02 was orthogonally confirmed to have a
bloodstream
infection by Enterobacter species. The panels depict normalized fragment
length distributions
for the sequences aligning to Enterobacter cloacae complex in nucleic acid
libraries
generated from plasma samples collected at different collection times
indicated above each
panel. FIG. 19B Subject RD-11 was orthogonally confirmed to have endocarditis
caused by
Staphylococcus aureus infection. The panels depict normalized fragment length
distributions
for the sequences aligning to Staphylococcus aureus in nucleic acid libraries
generated from
plasma samples collected at different collection times indicated above each
panel. FIG. 19C
Subject RD-13 was orthogonally confirmed to febrile neutropenia caused by
Escherichia coli
infection. The panels depict normalized fragment length distributions for the
sequences
aligning to Escherichia coli in nucleic acid libraries generated from plasma
samples collected
at different collection times indicated above each panel.
[0095] FIG. 20A depicts the fraction of reads outside of the "50 bp peak"
region (< 30
bp, and > 60 bp) as a function of the time post admission for fragment length
distributions of
all the orthogonally confirmed microbes. Shown are only the time traces for
the orthogonally
confirmed microbes where more than 50 unique sequences aligning to the
microbe's
references were detected. FIG. 20B depicts are the abundances in units of MPM
as a function
of the time post admission for all the orthogonally confirmed microbes that
were detected by
the method.
[0096] FIG. 21A shows pairs of orthogonally confirmed and orthogonally
unconfirmed microbes in the plasma sample collected at the admission time
point (t = 0) for
two subjects, RD-06 and RD-13. The orthogonally confirmed microbe in RD-06
(Staphylococcus aureus) is shown in the upper left panel. The unconfirmed
microbe in RD-06
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(Haemophilus influenzae) is shown in the lower left panel. The orthogonally
confirmed
microbe in RD-13 (Escherichia coli) is shown in the upper right panel. The
unconfirmed
microbe in RD-13 (Prevotella melaninogenica) is shown in the lower right
panel. FIG. 21B
The normalized fragment length distributions for Enterococcus gallinarum, an
orthogonally
unconfirmed microbe detected at several post-admission time points in plasma
samples
collected from subject RD-15. The time points are indicated above the panels.
[0097] FIG. 22 depicts the three main modes of the response of the human
fragment
length distribution during a treatment of an infected subject. The left panel
shows an example
where the long human fraction (> 60 bp) decreased during the treatment. The
middle panel
shows an example where the long human fraction (>60 bp) fluctuated during the
treatment.
The right panel shows an example where the long human fraction (>60 bp)
increased during
the treatment.
[0098] FIG. 23 provides a summary of fragment length information and GC
content
for samples from Streptococcus pasteuranius. Relative frequency is shown on
the y axis; GC
content is shown on the x-axis. Fragment length ranges of less than 45 base
pairs, 45-54 base
pairs, 55-64 base pairs, 65-74 base pairs, and longer than 74 base pairs are
shown. The
fragment length distribution in combination with the GC content information
suggests a
process induced temperature bias for this microbe.
DETAILED DESCRIPTION
[0099] Next generation sequencing (NGS) can be used to gather massive
amounts of
data about the nucleic acid content of a sample. It can be particularly useful
for analyzing
nucleic acids in complex samples, such as clinical samples. Heretofore, these
NGS systems
focused on determining the abundance of individual reads. The primary
properties of interest
prior to this work has been the sequence of each read and the abundance of
reads associated
with a particular source. This is particularly true for microbial nucleic
acids, and cell-free
microbial nucleic acids. In part this has been due to the fact that previous
sample processing
required for many NGS systems often result in errors and biases particularly
for low
abundance nucleic acids. Karius developed methods of preparing nucleic acid
libraries from
initial samples that reduce bias in the recovery of the nucleic acid libraries
from an initial
sample or that allow correction of the bias. The reduced bias in the nucleic
acid libraries
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obtained from the initial samples has allowed development of fragment length
profiles and
methods of generating fragment length profiles for nucleic acid libraries or
target nucleic
acids within the nucleic acid libraries. There is a need for efficient and
accurate methods for
generating fragment length profiles for nucleic acid libraries. This need can
be seen, for
example, with respect to distinguishing between closely related microbes,
determining
whether a microbe is present as a pathogen or a commensal microorganism,
determining
microbe's biological relationship with a host, predicting infection or
colonization site in a
subject, monitoring transplant status, monitoring fetal development and
status, tumor
monitoring, monitoring the status and response of the immune system, and
monitoring
toxicity of a compound administered to a subject.
[00100] A fragment length profile comprises one or more fragment length
characteristics for a nucleic acid library or a subset of reads from within a
nucleic acid library.
A fragment length profile may comprise 1, 2, 3,4, 5, 6,7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 17,
18, 19, 20 or more fragment length characteristics. A weighted value may be
assigned to one
or more fragment length characteristics in a fragment length profile such that
one or more
fragment length characteristics may have equal or different weights or values
within the
fragment length profile. Fragment length characteristics include, but are not
limited to shape
of the distribution, segment amplitude, peak shape, the fragment count ratio
for two or more
segments, the height of helical phasing peaks, fragment count ratio at two
different fragment
lengths, ratio of fragment counts within two different fragment length ranges,
the fragment
length range within a segment, the ratio of maximum amplitudes for two or more
segments,
position of a peak or peaks, and fragment length distribution within a subset
of reads. It is
intended that ratios "between 2 or more segments" encompasses, but is not
limited to, two or
more segments from one nucleic acid library, two or more segments from two or
more nucleic
acid libraries, two or more segments of the same peak shape, two or more
segments of
different peak shapes, two or more segments from similar or different nucleic
acid library
types and two or more segments from similar or different subsets of reads from
a nucleic acid
library.
[00101] Distribution types include, but are not limited to, a single peak
shape, a
multiple peak shape, exponential or exponential-like distributions,
distributions inflated for
long or short fragments, flat or uniform distributions, complex distribution
shapes and
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combinations thereof. Complex distribution may include aspects of at least 2,
at least 3, at
least 4, at least 5, at least 6, at least 7, at least 8 or more peak shapes. A
single peak shape
may occur around any fragment length including but not limited to around the
50 base pair
fragment length. Long fragments may include fragment lengths greater than
about 60 base
pairs, about 65 base pairs, about 70 base pairs, about 75 base pairs, about 80
base pairs, about
85 base pairs, about 90 base pairs, about 95 base pairs, about 100 base pairs,
about 150 base
pairs, about 175 base pairs, about 200 base pairs, about 250 base pairs, about
300 base pairs,
about 350 base pairs and about 400 base pairs. Short fragments may include
fragment lengths
shorter than about 500 bp, about 400 bp, about 300 bp, about 200 bp, about 100
bp, about 50
bp, about 40 bp, about 35 bp, about 30 bp, about 25, about 20 bp. Aspects of
peak shape
include but are not limited to the segment range, segment amplitude and the
total number of
reads within the segment, peak width, slope of the peak, derivative of the
peak; aspects of
peak shape may vary.
[00102] A single peak shape distribution may encompass a range of
fragment
lengths including but not limited to at least about 5 base pairs, at least
about 10 base pairs, at
least about 15 base pairs, at least about 20 base pairs, at least about 30
base pairs, at least
about 35 base pairs, at least about 40 base pairs, or more than at least about
a 45 base pair
fragment length range within a segment. Fragment length range within a segment
may vary.
For example, the range of fragment length around a 50 base pair single peak
distribution
includes but is not limited to fragment lengths from 30 to 60 base pairs, 35
to 60 base pairs,
40 to 60 base pairs, and 45 to 55 base pairs.
[00103] Segment amplitude encompasses the abundance or relative abundance
of reads
for a fragment length within a defined segment. In some aspects the
distribution amplitude
may be the highest abundance or relative abundance within a defined fragment
length range;
distribution amplitude may also encompass the average highest abundance or
relative
abundance within a defined fragment length range. In some aspects of the
application, a
fragment length distribution or fragment length distribution profile is
obtained for a subset of
reads from a nucleic acid library. A subset of reads from a nucleic acid
library is intended to
encompass less than the full set of reads from a nucleic acid library. Subsets
may reflect
reads determined to be from a particular microbe type, from particular microbe
species, host
reads, maternal reads, fetal reads, organ donor reads, non-host reads,
microbial cell-free
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nucleic acid reads, cell-free nucleic acid reads, microbial reads or any other
group;
alternatively, a subset of reads may reflect the full set of reads minus those
from a particular
microbe type, maternal read, fetal read or any other group. In some aspects of
the application,
a fragment length distribution is obtained for target nucleic acids. "Target
nucleic acids" can
be nucleic acid fragments derived from microbes, transplanted organ, tumor
cells, cancer
cells, host or non-host mitochondrial DNA, antibiotic resistance gene
sequences, host
genomic DNA, microbial sequences integrated into the host genome or any other
sequence or
sequences of interest in a nucleic acid library. A target sequence may have
migrated from
another site, such as a site of infection or donated organ.
[00104] In some cases, the target nucleic acid may make up only a very
small portion
of the entire sample, e.g., less than 0.1 %, less than 0.01%, less than
0.001%, less than
0.0001%, less than 0.00001%, less than 0.000001%, less than 0.0000001% of the
total nucleic
acids in a sample. Often, the total nucleic acids in an original sample may
vary. For example,
total cell-free nucleic acids (e.g., DNA, mRNA, RNA) may be in a range of 0.01-
10,000
ng/ml, e.g., (about 0.01, 0.1, 1, 5, 10, 20, 30, 40, 50, 80, 100, 1000, 5000,
10000 ng/ml). In
some cases, the total concentration of cell-free nucleic acids in a sample is
outside of this
range (e.g., less than 0.01 ng/ml; in other cases, the total concentration is
greater than 10,000
ng/ml). This may be the case with cell-free nucleic acid (e.g., DNA) samples
that are
predominantly made up of human DNA and/or RNA. In such samples, pathogen
target
nucleic acids may have scant presence compared to the human or host nucleic
acids.
[00105] The length of target nucleic acids can vary. In some particular
embodiments,
the target nucleic acids are relatively short; in other embodiments, the
targets are relatively
long. In some particular embodiments, the target nucleic acids are shorter
than 110 bp.
[00106] As used herein, "nucleic acid" refers to a polymer or oligomer of
nucleotides
and is generally synonymous with the term "polynucleotide" or
"oligonucleotide." Nucleic
acids may comprise, consist of, or consist essentially of a
deoxyribonucleotide, a
ribonucleotide, a deoxyribonucleotide analog, chemically modified canonical
deoxyribonucleotides, ribonucleotides, and/or ribonucleotide analog, nucleic
acids with
modified backbones, or any combination thereof.
[00107] Nucleic acids may be any type of nucleic acid including but not
limited to:
double-stranded (ds) nucleic acids, single stranded (ss) nucleic acids, DNA,
RNA, cDNA,
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mRNA, cRNA, tRNA, ribosomal RNA, dsDNA, ssDNA, miRNA, siRNA, short hairpin
RNA, circulating nucleic acids, circulating cell-free nucleic acids,
circulating DNA,
circulating RNA, cell-free nucleic acids, cell-free DNA, cell-free RNA,
circulating cell-free
DNA, cell-free dsDNA, cell-free ssDNA, circulating cell-free RNA, genomic DNA,
exosomes, cell-free pathogen nucleic acids, circulating microbe or pathogen
nucleic acids,
mitochondrial nucleic acids, non-mitochondrial nucleic acids, nuclear DNA,
nuclear RNA,
chromosomal DNA, circulating tumor DNA, circulating tumor RNA, circular
nucleic acids,
circular DNA, circular RNA, circular single-stranded DNA, circular double-
stranded DNA,
plasmids, bacterial nucleic acids, fungal nucleic acids, parasite nucleic
acids, viral nucleic
acids, cell-free bacterial nucleic acids, cell-free fungal nucleic acids, cell-
free parasite nucleic
acids, viral particle-associated nucleic acids, mitochondrial DNA, host
nucleic acids, host
cell-free nucleic acids, intercellular signal nucleic acids, exogenous nucleic
acids, DNA
enzymes, RNA enzymes, therapeutics nucleic acids, or any combination thereof.
Nucleic
acids may be nucleic acids derived from microbes or pathogens including but
not limited to
viruses, bacteria, fungi, parasites and any other microbe, particularly an
infectious microbe or
potentially infectious microbe. Nucleic acids may derive from archaea,
bacteria, fungi,
molds, eukaryotes, and/or viruses. In some embodiments, nucleic acids may be
derived
directly from the subject or host, as opposed to a microbe or pathogen.
[00108] As used
herein, a "nucleic acid library" refers to a collection of nucleic acid
fragments. The collection of nucleic acid fragments may be used, for example,
for
sequencing. A nucleic acid library may be prepared from an initial sample
using a bias-
corrected recovery method of generating a sequencing library or using a biased
recovery
method of generating a sequencing library enabling bias correction. As used
herein "bias-
corrected recovery" methods are methods with consistent fragment length
production that
generally recover sample nucleic acids fragments within a targeted length and
GC range
without appreciable length and GC bias, methods enabling bias correction,
methods capable
of accounting for the bias from a sample, and methods capable of accounting
for a bias
introduced by a process of the method of generating a nucleic acid library.
Bias-corrected
recovery methods may include but are not limited to adding a process control
molecule,
extracting, generating a library, sequencing, amplifying, and any combination
thereof.
Unbiased recovery methods include, but are not limited to those described in
U.S. Provisional
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No: 62/770,181 and 62/644,357. Methods of generating a nucleic acid library
from an initial
sample without extracting the nucleic acids before starting the nucleic acid
library generation
process from the initial sample are provided. In some embodiments, substances
that may
decrease yield or inhibit generation of a nucleic acid library, themselves,
may be extracted or
removed, but the nucleic acids are not extracted from the initial sample
before the nucleic acid
library is generated. The method comprises, consists of, or consists
essentially of adding one
or more process control molecules to an initial sample and generating the
nucleic acid library
from the spiked initial sample. The method comprises, consists of, or consists
essentially of
generating the nucleic acid library from the spiked initial sample. Nucleic
acid libraries may
utilize single-stranded and/or double-stranded nucleic acids.
[00109] Methods of generating a nucleic acid library from a sample with
extraction are
also encompassed.
[00110] Process control molecules can be one or more of ID Spike(s),
SPANKs, Sparks
or GC Spike-in Panel, dephosphorylation control molecules, denaturation
control molecules,
and/or ligation control molecules. See for example Published U.S. Patent
Application No.
2015-0133391 and Published U.S. Patent Application No. 2017-0016048, the full
disclosures
of each is incorporated herein by reference in its entirety for all purposes).
In some
embodiments, the initial sample comprises, consists of, or consists
essentially of circulating
donor nucleic acids (See, for example, US 20150211070, which is incorporated
by reference
herein in its entirety, including any drawings).
[00111] As used herein, "denaturing" refers to a process in which
biomolecules, such
as proteins or nucleic acids, lose their native or higher order structure.
Native and higher
order structure may include, for example, without limitation, quartenary
structure, tertiary
structure, or secondary structure. For example, a double-stranded nucleic acid
molecule can
be denatured into two single-stranded molecules.
[00112] As used herein, the term "dephosphorylation" or "dephosphorylating"
refers to
removal of a phosphate, such as the 5'- and/or 3'-end phosphate, from a
nucleic acid, such as
e.g. DNA.
[00113] As used herein, "detect" refers to quantitative or qualitative
detection,
including, without limitation, detection by identifying the presence, absence,
quantity,
frequency, concentration, sequence, form, structure, origin, or amount of an
analyte.
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[00114] In some embodiments, attaching a 3'-end adapter to nucleic acids,
for example,
denatured or dephosphorylated nucleic acids, and/or attaching a 5'-end adapter
comprises,
consists of, or consists essentially of ligating with an enzyme comprising,
consisting of, or
consisting essentially of a ligase, e.g., a T4 DNA ligase, CircLigase II. In
some embodiments,
the ligase is a single-stranded ligase. In some embodiments, attaching a 3'-
end adapter to
nucleic acids, for example, denatured or dephosphorylated nucleic acids,
and/or attaching a
5'-end adapter comprises, consists of, or consists essentially of utilizing
template-switching
reaction. In some embodiments, attaching a 3'-end adapter to nucleic acids,
for example,
denatured or dephosphorylated nucleic acids comprises, consists of, or
consists essentially of
extending with an enzyme comprising, consisting of, or consisting essentially
of a
polymerase, e.g., a TdT polymerase. In some embodiments, the method further
comprises,
consists of, or consists essentially of utilizing a DNA polymerase, e.g.,
Klenow fragment,
SuperScript IV reverse transcriptase, SMART MMLV Reverse Transcriptase, etc.
to extend a
primer hybridized to nucleic acids or adapted nucleic acids and to generate
complementary
strands. In some embodiments, a target nucleic acid may be attached to one or
more adapters.
In some embodiments, a target nucleic acid is attached to the same adapter or
different
adapters at both ends.
[00115] As used herein, "GC-bias" refers to differential performance,
treatment, or
recovery of nucleic acids of different GC content but having identical length.
[00116] As used herein, "GC-content" or "guanine-cytosine content" refer to
the
percentage of nitrogenous bases in a nucleic acid, such as a DNA or RNA
molecule, that are
either guanine or cytosine or their chemical modifications.
[00117] As used herein, "host" refers to an organism that harbors another
organism.
The latter is defined as "non-host" organism. For example, a human can be a
host that harbors
a microbe, pathogen or fetus, the microbe, pathogen or fetus being the non-
host. Host nucleic
acids or materials are derived from a host. Non-host nucleic acids or
materials may derive
from a non-host organism, from transplanted material or from a fetus or fetal
material within a
host.
[00118] As used herein, "microbe," "microbial," or "microorganism" refers
to an
organism, such as, for example, a microscopic or macroscopic organism, which
may exist as a
single cell or as a colony of cells, capsids, spores, filaments, or
multicellular organisms.
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Microbes include all unicellular organisms and some multicellular organisms,
such as, for
example, those from archaea, bacteria, protozoa, nematodes, viruses and
eukaryotes.
Microbes are often pathogens responsible for disease, but may also exist in a
non-pathogenic,
symbiotic relationship with a host, such as a human. A "commensal
microorganism" is
intended to include microbes that exist in a non-pathogenic, symbiotic
relationship with a
host. A host organism may harbor multiple types of non-host organisms
simultaneously. In
co-infection a host organism harbors multiple types of non-host organisms. The
multiple
types of non-host organisms may include one or more pathogens, one or more
commensal
microorganisms, or at least one pathogen and at least one commensal
microorganism. The
methods of the current application may be used to distinguish between closely
related
microorganisms, distinguish between microbes present as a pathogen, a
commensal
microorganism, or as incidental but clinically unimportant microbes.
[00119] Microbes or pathogens may include archaea, bacteria, yeast, fungi,
molds,
protozoans, nematodes, eukaryotes, and/or viruses. Microbes or pathogens may
also include
DNA viruses, RNA viruses, culturable bacteria, additional fastidious and
unculturable
bacteria, mycobacteria, and eukaryotic pathogens (See, Bennett J.E., D., R.,
Blaser, M.J.
Mandell, Douglas, and Bennett's Principles and Practice of Infectious
Diseases; Saunders,
Philadelphia, PA, 2014; and Netter's Infectious Disease, 1st Edition, edited
by Elaine C. Jong,
MD and Dennis L. Stevens, MD, PhD (2015)). Microbes or pathogens may also
include any
of the microbes set forth in https://www.ncbi.nlm.nih.gov/genome/microbes/ or
https://www.ncbi.nlm.nih.gov/biosample/.
[00120] Examples of microbes are one or more of the species or strains from
one or
more of the following genera: Coniosporium, Hantavirus, Talaromyces,
Machlomovirus,
Betatetravirus, Raoultella, Aeromonas, Ephemerovirus, Empedobacter, Loa,
Macluravirus,
Stenotrophomonas, Alfamovirus, Rosavirus, Emmonsia, Aggregatibacter,
Orthopneumovirus,
Weeksella, Nairovirus, Salivirus, Weissella, Mosavirus, Gammapartitivirus,
Strongyloides,
Passerivirus, Erysipelatoclostridium, Bacillarnavirus, Iotatorquevirus,
Taenia, Trypanosoma,
Olsenella, Cladosporium, Rhizobium, Prevotella, Leclercia, Paracoccus,
Ilarvirus, Lagovirus,
Rasamsonia, Plasmodium, Acremonium, Chlamydia, Clonorchis, Vibrio, Bartonella,
Nakazawaea, Franconibacter, Anisakis, Norovirus, Nocardia, Solobacterium,
Parechovirus,
Avenavirus, Orthohepevirus, Aphthovirus, Hepandensovirus, Microbacterium,
Lichtheimia,
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Lomentospora, Achromobacter, Ipomovirus, Tsukamurella, Elizabethkingia,
Hepevirus,
Seadornavirus, Alternaria, Trueperella, Gammatorquevirus, Bifidobacterium,
Chrysosporium,
Thogotovirus, Curtovirus, Deltatorquevirus, Balamuthia, Mastrevirus,
Bdellomicrovirus,
Mupapillomavirus, Pseudozyma, Wickerhamiella, Aquamavirus, Alloscardovia,
Thielavia,
Idaeovirus, Henipavirus, Coxiella, Haemophilus, Gammacoronavirus, Negevirus,
Brevibacterium, Peptoniphilus, Alphacarmotetravirus, Nosema, Trichovirus,
Arenavirus,
Thermomyces, Necator, Waikavirus, Blosnavirus, Jonesia, Tetraparvovirus,
Emaravirus,
Plectrovirus, Sclerodarnavirus, Toxocara, Umbravirus, Burkholderia,
Chromobacterium,
Paracoccidioides, Brugia, Eragrovirus, Macrococcus, Absidia, Colletotrichum,
Inovirus,
Phycomyces, Wickerhamomyces, Acidaminococcus, Moraxella, Rothia, Phlebovirus,
Slackia,
Purpureocillium, Betapapillomavirus, Tupavirus, Cryspovirus, Saksenaea,
Erysipelothrix,
Kobuvirus, Mimoreovirus, Echinococcus, Mannheimia, Bergeyella, Cyclospora,
Xylanimonas, Leptospira, Finegoldia, Curvularia, Cryptosporidium, Babuvirus,
Pecluvirus,
Lambdatorquevirus, Pythium, Carlavirus, Entomobirnavirus, Kocuria, Anaplasma,
Ampelovirus, Avihepatovirus, Nepovirus, Rhodococcus, Bordetella, Mischivirus,
Scedosporium, Gardnerella, Maculavirus, Trichoderma, Aveparvovirus,
Salmonella,
Avastrovirus, Copiparvovirus, Trachipleistophora, Clostridioides, Nanovirus,
Siccibacter,
Leptotrichia, Citrivirus, Odoribacter, Sanguibacter, Novirhabdovirus,
Acremonium, Hafnia,
Chaetomium, Tenuivirus, Yokenella, Rubulavirus, Varicellovirus,
Alphamesonivirus,
Sicinivirus, Leuconostoc, Microvirus, Gallantivirus, Morbillivirus, Lolavirus,
Pantoea,
Hepatovirus, Nupapillomavirus, Metschnikowia, Barnavirus, Kytococcus,
Tritimovirus,
Tannerella, Respirovirus, Pneumocystis, Dirofilaria, Pediococcus, Lactococcus,
Blastomyces,
Dianthovirus, Actinobacillus, Teschovirus, Oscivirus, Begomovirus, Potyvirus,
Byssochlamys, Alphacoronavirus, Molluscipoxvirus, Lymphocryptovirus,
Sapelovirus,
Parabacteroides, Pyrenochaeta, Listeria, Senecavirus, Brevidensovirus,
Potexvirus,
Parvimonas, Flavivirus, Recovirus, Toxoplasma, Yatapoxvirus, Opisthorchis,
Trichuris,
Cyphellophora, Morganella, Perhabdovirus, Micrococcus, Pequenovirus,
Mastadenovirus,
Anaeroglobus, Tropheryma, Dolosigranulum, Wolbachia, Lelliottia, Mycoplasma,
Tobravirus, Shewanella, Paeniclostridium, Erythroparvovirus, Sutterella,
Sporopachydermia,
Narnavirus, Nyavirus, Francisella, Arthroderma, Epsilontorquevirus,
Sigmavirus,
Amdoparvovirus, Actinomyces, Alphapermutotetravirus, Cardiobacterium,
Influenzavirus C,
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Orthopoxvirus, Poacevirus, Phialophora, Lactobacillus, Polyomavirus,
Debaryomyces,
Foveavirus, Bymovirus, Mycoflexivirus, Grimontia, Mucor, Rhytidhysteron,
Quadrivirus,
Thermoascus, Aureusvirus, Trichosporon, Myceliophthora, Dermacoccus,
Dysgonomonas,
Pseudoramibacter, Becurtovirus, Gordonia, Sapovirus, Orthobunyavirus,
Spiromicrovirus,
Pomovirus, Exophiala, Sneathia, Helicobacter, Photorhabdus, Mogibacterium,
Betapartitivirus, Avibirnavirus, Ambidensovirus, Oleavirus, Orientia,
Deltacoronavirus,
Anulavirus, Trichomonasvirus, Budvicia, Geotrichum, Enamovirus,
Lachnoclostridium,
Schistosoma, Paecilomyces, Panicovirus, Rhizoctonia, Brevibacillus, Beauveria,
Pestivirus,
Tombusvirus, Cilevirus, Cokeromyces, Peptostreptococcus, Phanerochaete,
Proteus,
Idnoreovirus, Aspergillus, Pasteurella, Malassezia, Hanseniaspora,
Endornavirus,
Azospirillum, Velarivirus, Cystovirus, Avisivirus, Bacteroides,
Picobirnavirus, Myroides,
Circovirus, Arterivirus, Aquaparamyxovirus, Onchocerca, Cosavirus,
Kluyveromyces,
Fijivirus, Candida, Hepacivirus, Dermabacter, Ourmiavirus, Allexivirus,
Enterobacter,
Acidovorax, Bracorhabdovirus, Carmovirus, Pluralibacter, Coltivirus,
Fonsecaea,
Streptobacillus, Corynebacterium, Macrophomina, Marburgvirus, Comovirus,
Fabavirus,
Alphanodavirus, Cellulomonas, Enterobius, Catabacter, Moellerella,
Nakaseomyces,
Cucumovirus, Valsa, Deltapartitivirus, Plesiomonas, Pseudomonas, Torovirus,
Cuevavirus,
Hypovirus, Trichomonas, Influenzavirus D, Giardiavirus, Crinivirus, Tepovirus,
Sakobuvirus,
Cyberlindnera, Paenalcaligenes, Bafinivirus, Rymovirus, Pegivirus, Yarrowia,
Treponema,
Borreliella, Rubivirus, Aureobasidium, Angiostrongylus, Filobasidium,
Photobacterium,
Rhizopus, Orthoreovirus, Ustilago, Simplexvirus, Aquareovirus,
Protoparvovirus,
Propionibacterium, Sprivivirus, Hunnivirus, Apophysomyces, Meyerozyma,
Alphapapillomavirus, Candida, Brucella, Gallivirus, Dinovernavirus,
Anaerobiospirillum,
Eubacterium, Tatlockia, Terrisporobacter, Quaranjavirus, Sobemovirus,
Dicipivirus,
Arcanobacterium, Macanavirus, Atopobium, Vesivirus, Lodderomyces,
Dinornavirus,
Betatorquevirus, Kerstersia, Aparavirus, Neisseria, Agrobacterium,
Edwardsiella,
Labyrnavirus, Totivirus, Actinomadura, Tobamovirus, Influenzavirus B,
Mandarivirus,
Anaerococcus, Kunsagivirus, Naegleria, Campylobacter, Veillonella, Yamadazyma,
Filobasidiella, Oerskovia, Penicillium, Anncaliia, Leptosphaeria, Pneumovirus,
Psychrobacter, Isavirus, Granulicatella, Torradovirus, Cladophialophora,
Influenzavirus A,
Ophiostoma, Aerococcus, Ureaplasma, Etatorquevirus, Bocaparvovirus,
Megasphaera,
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Reptarenavirus, Comamonas, Capnocytophaga, Alphatorquevirus, Syncephalastrum,
Wallemia, Betacoronavirus, Hyphopichia, Nocardiopsis, Legionella, Trichinella,
Paraburkholderia, Mammarenavirus, Echinostoma, Sphingobacterium, Enterovirus,
Methanobrevibacter, Ochroconis, Cheravirus, Pasivirus, Enterococcus,
Mycoreovirus,
Tospovirus, Betanodavirus, Phytoreovirus, Enterocytozoon, Ferlavirus,
Stemphylium,
Filifactor, Leishmaniavirus, Gemella, Bromovirus, Alloiococcus,
Cunninghamella,
Cronobacter, Oribacterium, Orbivirus, Chrysovirus, Cripavirus, Tatumella,
Pandoraea,
Ogataea, Dracunculus, Volvariella, Iflavirus, Benyvirus, Rhadinovirus,
Histoplasma,
Rahnella, Morococcus, Verticillium, Janibacter, Gyrovirus, Alphapartitivirus,
Mycobacterium, Roseomonas, Varicosavirus, Chryseobacterium, Parapoxvirus,
Rhizomucor,
Aureimonas, Levivirus, Leishmania, Luteovirus, Cypovirus, Ochrobactrum,
Microsporum,
Piscihepevirus, Ceratocystis, Sporothrix, Vesiculovirus, Cupriavidus,
Cryptococcus,
Metapneumovirus, Alphanecrovirus, Eikenella, Brevundimonas, Escherichia,
Leifsonia,
Schizophyllum, Granulibacter, Gordonibacter, Lachancea, Madurella, Ophiovirus,
Phellinus,
Nebovirus, Acanthamoeba, Fusobacterium, Pichia, Verruconis, Ehrlichia,
Tibrovirus,
Higrevirus, Wohlfahrtiimonas, Rhinocladiella, Neorickettsia, Sadwavirus,
Roseobacter,
Sequivirus, Pannonibacter, Rotavirus, Turicella, Cardiovirus,
Propionimicrobium, Furovirus,
Naumovozyma, Closterovirus, Fluoribacter, Zeavirus, Clavispora, Megrivirus,
Gammapapillomavirus, Rickettsia, Polemovirus, Corynespora, Encephalitozoon,
Shimwellia,
Fusarium, Yersinia, Capronia, Delftia, Victorivirus, Marafivirus, Kluyvera,
Iteradensovirus,
Isoptericola, Vitivirus, Roseolovirus, Conidiobolus, Abiotrophia, Babesia,
Phoma,
Sanguibacteroides, Staphylococcus, Rhodotorula, Zetatorquevirus, Hymenolepis,
Fasciola,
Cytorhabdovirus, Cardoreovirus, Memnoniella, Trichophyton, Mitovirus,
Phaeoacremonium,
Providencia, Lysinibacillus, Giardia, Oligella, Streptomyces, Paraclostridium,
Ralstonia,
Coccidioides, Brambyvirus, Biatriospora, Allolevivirus, Acinetobacter,
Starmerella,
Omegatetravirus, Porphyromonas, Avulavirus, Streptococcus, Arcobacter,
Topocuvirus,
Mamastrovirus, Ancylostoma, Bornavirus, Capillovirus, Alphavirus, Tymovirus,
Nucleorhabdovirus, Diaporthe, Chlamydiamicrovirus, Turncurtovirus,
Saccharomyces,
Riemerella, Betanecrovirus, Clostridium, Mobiluncus, Cercospora, Marnavirus,
Mortierella,
Aquabirnavirus, Xanthomonas, Dependoparvovirus, Ebolavirus, Neofusicoccum,
Borrelia,
Leminorella, Klebsiella, Blastocystis, Alcaligenes, Citrobacter, Eggerthella,
Cedecea,
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Serratia, Penstyldensovirus, Bacillus, Laribacter, Wuchereria, Hordeivirus,
Cytomegalovirus,
Actinomucor, Ascaris, Shigella, Vittaforma, Torulaspora, Kingella, Oryzavirus,
Polerovirus,
Tremovirus, Erbovirus, Entamoeba, Lyssavirus, Paenibacillus, Facklamia,
Kappatorquevirus,
Metarhizium, Stachybotrys, Okavirus, Botrexvirus, Thetatorquevirus, and
Basidiobolus.
[00121] As used herein, infection stage or stage of infection refers to the
invisible
phase of infection, the symptomatic phase of an infection, the resolution
phase of an infection,
the treatment phase, a recurrent phase, a recrudescent phase, an acute phase
or infection, a
chronic phase or infection, a slow or latent phase or infection, a persistent
infection, a
disseminated infection stage, a primary phase, a secondary phase or a tertiary
phase of
infection. The invisible phase of an infection occurs prior to emergence of
the symptoms or
before the symptoms are noticed by the subject or others. Synonyms of
"invisible phase"
would include "pre-symptomatic infection stage", "nascent stage of an
infection" and "early
stage of infection". A commensal organism may persist in the invisible stage
of infection.
The symptomatic phase of an infection occurs when the subject or others notice
the symptoms
or clinical change such as for example fever, pain, rash, headache, aches,
respiratory
problems, etc. The resolution phase of an infection during which an infection
resolves by
itself or by administering a treatment. The treatment phase may be part of the
resolution
phase if a treatment is administered. A recurrent phase occurs if a subject
experiences a
recurrence of an infection in any of the above stages. A recrudescent phase
occurs if the
infection is not treated properly or sufficiently the first time and comes
back. Chronic
infection are a type of persistent infection that is eventually cleared. An
acute phase or
infection occurs suddenly such as Hepatitis. A slow or latent phase or
infection is an infection
that lasts for the rest of the life of the host. A persistent infection is an
infection that lasts for
long periods; persistent infections occur when the primary infection is not
cleared by the host.
Some microbes infect hosts with primary, secondary and tertiary phase
infections; an example
is infection by Treponema pallidum. An infection may stay at any of the above
stages for an
indefinite period of time without necessarily progressing to a different
phase. A commensal
or symbiotic microbe may remain in the invisible stage of infection
indefinitely or may not
infect.
[00122] A variety of host-microbe biological relationships or interactions
are known in
the art. Host-microbe biological interactions include but are not limited to
commensalism,
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mutualism, amensalism, parasitism, symbiosis and competition. It is recognized
that a
microbe may exhibit one type of interaction with the host when it is localized
to certain sites
but may exhibit another type of interaction with the host when it is localized
to another site
within the host. For example a microbe may exist in a commensalistic
relationship to the host
on the skin of the host but could exist in a parasitic or competitive
relationship internal to the
host. As used herein, "pathogen" refers to a microorganism that causes, or can
cause, or is
suspected to cause disease.
[00123] As used herein, the phrase "spiked initial sample" refers to an
initial sample to
which process control molecules have been added prior to the start of
generating a sequencing
library.
[00124] The term "derived from" encompasses the terms "originated from,"
"obtained
from," "obtainable from," and "created from," generally indicates that one
specified material
finds its origin in another specified material or has features that can be
described with
reference to the another specified material. For example, an initial sample
may be derived
from a raw biological sample.
[00125] In some embodiments, the initial sample comprises, consists of, or
consists
essentially of a solid or a body fluid such as blood, plasma, serum,
cerebrospinal fluid,
synovial fluid, bronchoalveolar lavage, urine, stool, saliva, abdominal fluid,
ascites fluid,
peritoneal lavage, gastric fluid, interstitial fluid, lymph fluid, bile,
abscess fluid, tissue,
amniotic fluid, meconium, sinus aspirate, lymph node, bone marrow, hair,
nails, cheek swab,
skin swab, urethral swab, cervical swab, nasopharyngeal swab, nasopharyngeal
aspirate,
vaginal swab, epithelial cells, semen, vaginal discharge, intercellular fluid,
pericardial fluid,
rectal swab, bone, skin tissue, soft tissue, tears, and/or a nasal sample. In
some embodiments,
the initial sample comprises, consists of, or consists essentially of plasma.
In some
embodiments, the initial sample comprises, consists of, or consists
essentially of urine. In
some embodiments, the initial sample comprises, consists of, or consists
essentially of
cerebrospinal fluid. In some embodiments, the initial sample is from a human
subject.
[00126] In some embodiments, an initial sample can be made up of, in whole
or in part,
cells and/or tissue. The initial sample may be cell-free or cell-depleted. The
initial cell-free
sample may comprise, consist of, or consist essentially of nucleic acids that
originated from a
different site in the body, such as a site of pathogenic infection. In the
case of blood, serum,
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lymph, or plasma, the cell-free sample or cell-depleted initial sample may
contain
"circulating" cell-free nucleic acids that originated at anatomic locations
other than the site of
bodily fluid collection of the fluid in question. In the case of urine, the
cell-free nucleic acids
may be cell-free nucleic acids that originated in a different site in the
body. The cell-free
samples or cell-depleted initial samples can be obtained by depleting or
removing cells, cell
fragments, or exosomes by a known technique such as by centrifugation or
filtration.
[00127] As used herein, the term "invasive disease" refers to a disease
based, in part, on
the ability of particular pathogens to seriously compromise the health of
certain infected
subjects, as opposed to merely colonizing other infected subjects, either as a
commensal or
infection with no or minor symptoms. For example, certain microbes can locally
colonize
tissues without causing any health problems in some hosts, while, in other
hosts, they may
invade tissues to the point where they cause serious inflammation, tissue or
organ damage,
sepsis, cancer, and other serious health issues. Microbes may also colonize a
subject who is
asymptomatic at one time point, but at a later point develops serious symptoms
when the
microbe translocates and/or becomes "active."
[00128] As used herein, the term "cell-free" refers to the condition of the
nucleic acid
outside a cell, viral particle or virion as it appeared in the body
immediately before the sample
is obtained from the body. For example, circulating cell-free nucleic acids in
a sample may
have originated as cell-free nucleic acids circulating in the bloodstream of a
subject. In
contrast, nucleic acids that are extracted post-collection from an intact
microorganism, such as
a blood-borne pathogen, or removed post-collection from intact virions in a
plasma sample,
are generally not considered to be "cell-free."
[00129] The present application provides methods of determining a site of
localization
in a subject. Nucleic acids from microbes or microoganisms from different
sites within a
subject may exhibit different fragment length profiles. The fragment length
profile of a
nucleic acid library or a subset of the nucleic acid library containing
microbial nucleic acids
differs if the microbial infection is circulating rather than located at one
or more sites of
localization. Thus, comparing a fragment length profile to a reference
fragment length profile
of one or more source sites may predict a site of localization if the fragment
length profile
from the sample is similar to a reference fragment length profile from a
source site. By "site
of localization" is intended any source site within a subject where a microbe
occurs, persists,
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survives or proliferates. Source sites include, but are not limited to the
bloodstream, blood,
deep tissue, such as but not limited to the kidneys, liver, stomach, bladder,
digestive organs,
nerve cells, lung, bone, brain, heart, heart lining, sinus, GI tract, spleen,
skin, joint, ear, nose,
and mouth. It is envisioned that a subject may have more than one site of
localization for a
particular microbe. It is further understood that some sites of localization
for a particular
microbe may not contribute to a disease state or condition. Rather, some sites
of localization
for a particular microbe may indicate a commensal relationship between the
microbe and host,
while other sites of localization for a particular microbe may indicate a
parasitic or amensal
relationship between the microbe and host. It is further recognized that the
occurrence of
multiple sites of localization for a particular microbe may indicate a
systemic infection of the
host. Additionally it is recognized that site of localization for a particular
microbe or
pathogen of interest may impact a decision to treat or not to treat and may
impact selection of
appropriate treatment options. For example and without being limited by
mechanism a fungal
pathogen localized to the skin may be treated differently than a fungal
pathogen localized to
the lung and a bacterial microbe localized to heart tissue including but not
limited to the lining
of the hear may be treated differently than a bacterial microbe localized to
the blood or blood
stream.
[00130] In some embodiments, an initial sample comprises, consists of, or
consists
essentially of circulating tumor or fetal nucleic acids. (See, for example,
Analysis of serum or
blood borne nucleic acids, such as circulating tumor or fetal nucleic acids,
e.g., as described in
U.S. Patent Nos. 8,877,442 and 9,353,414, or in pathogen identification
through, e.g., analysis
of circulating microbial or viral nucleic acids, e.g., as described in
Published U.S. Patent
Application No. 2015-0133391 and Published U.S. Patent Application No. 2017-
0016048, the
full disclosures of each is incorporated herein by reference in its entirety
for all purposes). In
some embodiments, the initial sample comprises, consists of, or consists
essentially of
circulating donor nucleic acids (See, for example, US 20150211070, which is
incorporated by
reference herein in its entirety, including any drawings).
[00131] An initial sample can be derived from any subject (e.g., a human
subject, a
non-human subject, etc.). The subject can be healthy. In some embodiments, the
subject is a
human patient having, suspected of having, or at risk of having, a disease or
infection. In
some embodiments, the disease or infection is pathogen-related.
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[00132] A human subject can be a male or female. In some embodiments, the
sample
can be from a human embryo or a human fetus. In some embodiments, the human
can be an
infant, child, teenager, adult, or elderly person. In some embodiments, the
subject is a female
subject who is pregnant, suspected of being pregnant, or planning to become
pregnant.
[00133] In some embodiments, the subject is a human subject who has
undergone an
organ transplant or who is planning to undergo organ transplant.
[00134] In some embodiments, the subject is a farm animal, a lab animal, or
a domestic
pet. In some embodiments, the animal can be an insect, a dog, a cat, a horse,
a cow, a mouse,
a rat, a pig, a fish, a bird, a chicken, or a monkey.
[00135] The subject can be an organism, such as a single-celled or multi-
cellular
organism. In some embodiments, the sample may be obtained from a plant, fungi,
eubacteria,
archeabacteria, protist, or any multicellular organism. The subject may be
cultured cells,
which may be primary cells or cells from an established cell line.
[00136] In some embodiments, the subject has a genetic disease or disorder,
is affected
by a genetic disease or disorder, or is at risk of having a genetic disease or
disorder. A genetic
disease or disorder can be linked to a genetic variation such as mutations,
insertions,
additions, deletions, translocations, point mutations, trinucleotide repeat
disorders, single
nucleotide polymorphisms (SNPs), or a combination of genetic variations.
[00137] In some aspects, the subject is healthy or asymptomatic, or
exhibits mild or
non-specific clinical symptoms. In some cases a subject may be infected or
suspected of
being infected by a particular pathogen. In other cases, the subject is
suspected of having an
infection of unknown origin. In some cases the subject has been exposed to a
pathogen, or
suspected to have been exposed to a pathogen such as by living conditions, by
travel to a
particular geographic region or by interaction or sexual interaction with an
infected
individual.
[00138] The initial sample can be from a subject who has a specific
disease, condition,
or infection, or is suspected of having (or at risk of having) a specific
disease, condition, or
infection. For example, the initial sample can be from a cancer patient, a
patient suspected of
having cancer or a patient at risk of having cancer. In some embodiments, the
initial sample
can be from a patient with an infection, a patient suspected of an infection,
or a patient at risk
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of having an infection. In some embodiments, the initial sample is from a
subject who has
undergone, or will undergo, an organ transplant.
[00139] Primer extension reactions can be carried out with a DNA-dependent
polymerase or an RNA-dependent polymerase or reverse transcriptase or a
combination
thereof. In some embodiments, the primer extension reaction can be carried out
by a DNA or
RNA polymerase having strand displacing activity. In some embodiments, the
primer
extension reaction is carried out by a DNA or RNA polymerase that has non-
templated
activity. In some other embodiments, the primer extension reaction can be
carried out by a
DNA or RNA polymerase having strand displacing activity and a DNA or RNA
polymerase
that has non-templated activity. In some embodiments, primer extension is
carried out with a
Klenow fragment.
[00140] Reference fragment length profiles are generally predetermined.
Suitable
reference fragment length profile or profiles may vary depending on the
method, type of
comparison or purpose of method. One skilled in the art would select an
appropriate
reference fragment length profile or profiles. Reference fragment length
profiles may be
obtained from a subject or cell exposed to a compound of interest, a subject
or cell exposed to
a similar compound, from a subject or cell similar to said subject, from a
subject or cell
hosting a known microbe, from a subject or cell previously determined to have
an infection in
a source site, or a subject or cell in any other condition of interest
suitable for use as
determined by one skilled in the art.
[00141] Subjects with a transplant are at risk for transplant rejection
even when
provided with therapies to reduce the risk of rejection. Transplant rejection
and transplant
rejection disorder are significant, often life-threatening, risks to subjects
with a transplant.
Many anti-rejection therapies suppress the immune system of the subject thus
increasing the
subject's risk of infection or disease. Therefore there is a need to balance
the use and dose of
anti-rejection therapies. The current application provides methods of
monitoring transplant
status in a subject with a transplant. The methods comprise the steps of
generating a baseline
fragment length profile for a target nucleic acid within the nucleic acid
library or the whole
nucleic acid library generated from a sample obtained from said subject or
donor. Target
nucleic acids of particular interest in monitoring transplant status include,
but are not limited
to, donor and recipient mitochondrial DNA (mtDNA). Methods of monitoring
transplant
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status may further comprise evaluating abundance of mitochondrial DNA from the
transplant.
Monitoring transplant status encompass monitoring anything related to the
status of a
transplant including, but not limited to, host rejection of the transplant,
host immune reaction
to the transplant, host reaction to the transplant, transplant deterioration,
transplant health,
transplant vascularization, transplant oxygenation and transplant breakdown. A
baseline
fragment length profile may be generated from a donor and/or recipient sample
obtained
before transplant, upon transplant or after transplant. The methods further
comprise the step
of generating a second fragment length profile from a sample obtained from the
subject and
comparing the second fragment length profile to the baseline fragment length
profile. If the
second fragment length profile differs from the baseline fragment length
profile then an
increased amount of an anti-rejection therapy may be internally administered
to the subject.
[00142] Methods and systems of the present disclosure can be implemented by
way of
one or more algorithms. An algorithm can be implemented by way of software
upon
execution by a central processing unit. The algorithm can, for example,
facilitate the
enrichment, sequencing and/or detection of pathogen or microbe or other target
nucleic acids,
or generation of a fragment length profile.
[00143] A compound may include, but is not limited to a chemotherapeutic
agent, an
antiviral agent, an antibiotic agent, an anti-fungal agent, an agent of
interest, a small molecule,
an experimental agent, a clinical trial compound, a medicine, drug, and active
ingredient.
[00144] Toxicity includes but is not limited to cytotoxicity. It is further
recognized that
toxicity may occur preferentially in particular classes of cells including but
not limited to
cancer cells and pathogens.
[00145] The fragment length profiles and methods of the present application
may be
used in noninvasive prenatal testing (NIPT). The methods allow non-invasive
monitoring,
diagnosis and tracking of fetal condition.
[00146] In some embodiments, separating the adapted nucleic acids
comprises, consists
of, or consists essentially of immobilizing the adapted nucleic acids. In some
embodiments,
immobilization occurs on magnetic beads or functionalized magnetic beads. In
some
embodiments, immobilization occurs on a modified glass, modified capillary
surfaces, and/or
modified columns. In some embodiments, separating the adapted nucleic acids
comprises,
consists of, or consists essentially of purifying the adapted nucleic acids.
In some
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embodiments, separating the adapted nucleic acids comprises, consists of, or
consists
essentially of precipitating the adapted nucleic acids. In some embodiments,
separating the
adapted nucleic acids comprises, consists of, or consists essentially of using
a 3'-end
protected 3'-end adapter. In some embodiments, separating the adapted nucleic
acids
comprises, consists of, or consists essentially of separating adapted nucleic
acids from
unadapted nucleic acids by digesting unadapted nucleic acids with a 3'end
exonuclease, the
adapted nucleic acids comprising, consisting of, or consisting essentially of
a 3'-end protected
3'-end adapter. Some embodiments further comprise, consist of, or consist
essentially of
enriching nucleic acids for fragments of a certain length. In some
embodiments, denaturation
is used to further separate nucleic acids or target nucleic acids. In some
embodiments,
denaturation comprises, consists of, or consists essentially of selective
denaturation. In some
embodiments, selective denaturation comprises, consists of, or consists
essentially of one or
more denaturation steps effective for the selection of fragments of a certain
length and/or GC-
content. In some embodiments, separating for fragments of a certain length may
occur
through the use of proteinases, detergents, heparin, hemolysis and plasma
concentration.
[00147] The methods provided herein include various non-invasive methods
for
subject's suffering from an infection, a subject at risk for contracting an
infection, and/or for a
subject experiencing undefined symptoms that are mimicking multiple other
diseases. The
methods provided herein can be applied for a variety of purposes such as to
diagnose or detect
an infection, to determine the infection stage, to predict the infecting stage
of the microbe, to
predict if the infection will progress to an invasive disease stage, to
monitor the efficacy
and/or response to a treatment or procedure, to stop the treatment, to
determine the site of
infection, to determine the site of colonization, or to modify, or optimize a
therapy for a better
clinical response. Consequently, the methods provided herein may reduce
adverse effects
caused by a misdiagnosis or by an invasive procedure such as a biopsy to
determine if, what,
and how an organ has been infected in the subject.
[00148] FIG. 1 provides a general overview of some of the methods provided
herein.
Often, the methods can comprise, obtaining a clinical sample from an infected
subject or a
subject at risk of having an infection; making a "spiked-sample" by adding the
synthetic
nucleic acids provided by the disclosure; optionally, extracting the nucleic
acids from the
spiked-sample; generating a spiked-sample library; optionally, enriching for a
target nucleic
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acid of interest; conducting a detection assay, such as a sequencing assay to
obtain sequence
reads from the spiked-sample library; and determining a measurement from the
detected
nucleic acids, and comparing this measurement to a control or a reference to
determine the
infection stage, biological relationship between microbe and host, or site of
localization (e.g.,
organ, or tissue type) in a subject. In some cases, the comparison of the
absolute abundance
for a target nucleic acid to a control or reference can indicate an infection
stage or site of
localization in the subject. In some cases, the comparison of the distribution
of fragment
lengths for the target nucleic acid to a control or reference can indicate the
infection stage or
site of localization in the subject. In some cases, the comparison of the
absolute abundance
and distribution of fragment lengths for a target nucleic acid to a control or
reference can
indicate the infection stage or site of localization in the subject.
[00149] The methods provided herein can be applied to any type of nucleic
acid found
in a clinical sample. FIG. 2 provides an overview of an example of a cell-free
method. FIG.
17 provides a schematic of an exemplary infection in a subject. A source of a
pathogen
infection may be, for example in the lung or any other organ (e.g, brain,
skin, heart tissue,
stomach, liver, intestine). Cell-free nucleic acids, such as cell-free DNA,
derived from the
pathogen may travel through the bloodstream and can be collected in a plasma
sample for
analysis. Some of the cell-free methods provided herein can comprise obtaining
a clinical
sample from an infected subject or a subject at risk of having an infection;
making a "spiked-
sample" by adding the synthetic nucleic acids provided by the disclosure;
isolating the cell-
free nucleic acids, optionally, extracting the cell-free nucleic acids from
the spiked-sample;
generating a spiked-sample library; optionally, enriching for a target nucleic
acid of interest;
conducting a detection assay, such as a sequencing assay to obtain sequence
reads from the
spiked-sample library; and determining a measurement from the detected cell-
free nucleic
acids, and comparing this measurement to a control or a reference to determine
the infection
stage or site of localization in the subject.
[00150] In some cases, the methods may be combined with a sequencing method
to
identify an organ or tissue that may be infected, or to rule out the
possibility that an organ is
infected in a subject (see, Koh W. et al, Noninvasive in vivo monitoring of
tissue-specific
global gene expression in humans, PNAS 2014: 111(7361-7366), which publication
is hereby
incorporated by reference in its entirety for all purposes). FIG. 4 provides
an example of an
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organ-site method using cell-free RNA sequencing. An organ-site detection
assay may be
used in a case where the methods of the disclosure or another clinical test
determines that the
subject has an infection at the invasive disease stage. In this case, the
method may further
comprise conducting one of the organ-site methods provided herein to detect if
an organ has
been infected.
[00151] The present disclosure also provides methods for individualized
treatment for
an infected subject or a subject who is susceptible or at risk for infections
(e.g.,
immunosuppressed, immunocompromised, living conditions, or genetic variations
resulting in
increased susceptibility for infection). Individualized treatment provided by
the present
disclosure includes methods of predicting if an infection will progress to an
invasive disease
stage, methods for monitoring the efficacy of a therapy in a subject,
modifying a therapeutic
regimen depending on the subject's response to the therapy, and determining
the pathogen's
resistance to a particular therapeutic or a subject's genetic predisposition
for a response to a
given therapeutic.
[00152] The nucleic acids produced according to the present methods may be
analyzed
to obtain various types of information including genomic, epigenetic (e.g.,
methylation), and
RNA expression. Methylation analysis can be performed by, for example,
conversion of
methylated bases followed by DNA sequencing. RNA expression analysis can be
performed,
for example, by polynucleotide array hybridization, by RNA sequencing
techniques, or by
sequencing cDNA produced from RNA.
[00153] Sequencing may be by any method known in the art. Sequencing
methods
include, but are not limited to, Maxam-Gilbert sequencing-based techniques,
chain-
termination-based techniques, shotgun sequencing, bridge PCR sequencing,
single-molecule
real-time sequencing, ion semiconductor sequencing (e.g., Ion Torrent
sequencing), nanopore
sequencing, pyrosequencing (454), sequencing by synthesis, sequencing by
ligation (SOLiD
sequencing), sequencing by electron microscopy, dideoxy sequencing reactions
(Sanger
method), massively parallel sequencing, polony sequencing, and DNA nanoball
sequencing.
The term "Next Generation Sequencing (NGS)" herein refers to sequencing
methods that
allow for massively parallel sequencing of nucleic acid molecules during which
a plurality,
e.g., millions, of nucleic acid fragments from a single sample or from
multiple different
samples are sequenced simultaneously. Non-limiting examples of NGS include
sequencing-
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by-synthesis, sequencing-by-ligation, real-time sequencing, and nanopore
sequencing. In
some embodiments, sequencing involves hybridizing a primer to the template to
form a
template/primer duplex, contacting the duplex with a polymerase enzyme in the
presence of
detectably labeled or unlabeled nucleotides under conditions that permit the
polymerase to
add labeled or unlabeled nucleotides to the primer in a template-dependent
manner, detecting
a signal from the incorporated labeled nucleotide or detecting a signal
resulting from the
process of incorporating labeled or unlabeled nucleotide (e.g., proton
release), and
sequentially repeating the contacting and/or detecting steps at least once,
wherein sequential
detection of incorporated labeled or unlabeled nucleotide determines the
sequence of the
nucleic acid.
[00154] Exemplary detectable labels include radiolabels, fluorescent
labels, protein
labels, dye labels, enzymatic labels, etc. In some embodiments, the detectable
label may be
an optically detectable label, such as a fluorescent label. Exemplary
fluorescent labels include
cyanine, rhodamine, fluorescein, coumarin, BODIPY, alexa, or conjugated multi-
dyes.
[00155] In some embodiments, the sequencing comprises, consists of, or
consists
essentially of obtaining paired end reads. In some embodiments, the sequencing
comprises,
consists of, or consists essentially of obtaining consensus reads.
[00156] The accuracy or average accuracy of the sequence information may be
greater
than about 80%, about 90%, about 95%, about 99%, about 99.98%, or about
99.99%. The
sequence accuracy or average accuracy may be greater than about 95% or about
99%. The
sequence coverage may be greater than about 0.00001 fold, 0.0001 fold, 0.001
fold, about
0.01 fold, about 0.1 fold, about 0.5 fold, about 0.7 fold, or about 0.9 fold.
The sequence
coverage may be less than about 200,000 fold, about 100,000 fold, about 10,000
fold, about
1,000 fold, or about 500 fold.
[00157] In some embodiments, the sequence information obtained per nucleic
acid
template is more than about 10 base pairs, about 15 base pairs, about 20 base
pairs, about 50
base pairs, about 100 base pairs, or about 200 base pairs. The sequence
information may be
obtained in less than 1 month, 2 weeks, 1 week, 2 days, 1 day, 14 hours, 10
hours, 3 hours, 1
hour, 30 minutes, 10 minutes, or 5 minutes.
[00158] Although the Examples (below) use specific sequences for certain
sequencing
systems, e.g., Illumina systems, it will be understood that the reference to
these sequences is
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for illustration purposes only, and the methods described herein may be
configured for use
with other sequencing systems incorporating specific priming, attachment,
index, and other
operational sequences used in those systems, e.g., systems available from Ion
Torrent, Oxford
Nanopore, Genia Technologies, Pacific Biosciences, Complete Genomics, and the
like.
[00159] The methods provided herein may include use of a system such as a
system
that contains a nucleic acid sequencer (e.g., DNA sequencer, RNA sequencer)
for generating
DNA or RNA sequence information. The system may include a computer comprising
software that performs bioinformatic analysis on the DNA or RNA sequence
information.
Bioinformatic analysis can include, without limitation, assembling sequence
data, detecting
and quantifying genetic variants in a sample, including germline variants and
somatic cell
variants (e.g., a genetic variation associated with cancer or pre-cancerous
condition, a genetic
variation associated with infection).
[00160] Sequencing data may be used to determine genetic sequence
information,
ploidy states, the identity of one or more genetic variants, as well as a
quantitative measure of
the variants, including relative and absolute relative measures.
[00161] In some cases, sequencing of the genome involves whole genome
sequencing
or partial genome sequencing. The sequencing may be unbiased and may involve
sequencing
all or substantially all (e.g., greater than 70%, 80%, 90%) of the nucleic
acids in a sample.
Sequencing of the genome can be selective, e.g., directed to portions of the
genome of
interest. Sequencing of select genes, or portions of genes may suffice for the
analysis desired.
Polynucleotides mapping to specific loci in the genome that are the subject of
interest can be
isolated for sequencing by, for example, sequence capture or site-specific
amplification.
Aligning Sequence Reads
[00162] Following sequencing, the dataset of sequences can be uploaded to a
data
processor for bioinformatics analysis to subtract host or host-related
sequences, e.g., human,
cat, dog, etc. from the analysis; and determine the presence and prevalence of
pathogen or
contaminant sequences (for example microbial sequences), for example by a
comparison of
the coverage of sequences mapping to a microbial reference sequence to
coverage of the host
reference sequence. The subtraction of host sequences may include the step of
identifying a
reference host sequence, and masking microbial sequences or microbial-
mimicking sequences
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present in the reference host genome. Similarly, determining the presence of a
microbial
sequence by comparison to a microbial reference sequence may include the step
of identifying
a reference microbial sequence, and masking host sequences or host-mimicking
sequences
present in the reference microbial genome sequences.
[00163] The dataset can be optionally cleaned to check sequence quality,
remove
remnants of sequencer specific nucleotides (for example adapter sequences),
and merge
paired end reads that overlap to create a higher quality consensus sequence
with less read
errors. Duplicate sequences can be identified as those having identical start
sites and length or
identical or almost identical sequence. Optionally, duplicates may be removed
from the
analysis.
[00164] In some aspects, host or host-related (e.g., human) sequences can
be subtracted
from the analysis. In some aspects, host sequences are retained in the
analysis. In some
aspects, the amplification/sequencing steps can be unbiased and the
preponderance of
sequences in a sample will be host sequences. The subtraction process may be
optimized in
several ways to improve the speed and accuracy of the process, for example by
performing
multiple subtractions where the initial alignment is set at a coarse filter,
e.g., with a fast
aligner, and performing additional alignments with a fine filter such as a
sensitive aligner or
extended reference database.
[00165] The dataset of reads can be initially aligned against a host
reference genome,
including without limitation Genbank hg19 or Genbank hg38 reference sequences,
to
bioinformatically subtract the host DNA. Each sequence can be aligned with the
best fit
sequence in the host reference sequence. Sequences identified as host can be
bioinformatically
removed from the analysis.
[00166] The removal of host or host-related sequences can also be optimized
by adding
in contigs that have a high hit rate, including without limitation highly
repetitive sequence
present in the genome that are not well represented in reference databases.
For example, it has
been observed that of the reads that do not align to hg19 or hg38, a
significant amount is
eventually identified as human in a later stage of the pipeline, when a
database that includes a
large set of human sequences is used, for example the entire NCBI NT database.
Removing
these reads earlier in the analysis can be performed by building an expanded
host or host-
related reference. This reference can be created by identifying host contigs
in a sequence
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database other than the reference, e.g., NCBI NT database, that have high
coverage after the
initial host read subtraction. Those contigs can be added to the host
reference to create a more
comprehensive reference set. Additionally, novel assembled host-related
contigs from cohort
studies can be used as a further reference to filter host-derived reads.
[00167] Regions of the host genome reference sequence that contain relevant
non-host
sequences may be masked, e.g., viral and bacterial sequences that are
integrated into the
genome of the reference sample.
[00168] Optionally, host or host-related sequences can be identified and
removed by
non-alignment based methods, such as identifying sequences by sequence
characteristics
including frequency of certain motifs, sequence patterns, word frequencies, or
nucleotide
biases.
[00169] Sequence reads identified as non-human can then be aligned to a
nucleotide
database of microbial reference sequences. The database may be selected for
those microbial
sequences known to be associated with the host, e.g., the set of human
commensal and
pathogenic microorganisms.
[00170] The microbial database may be optimized to mask or remove
contaminating
sequences. For example, many public database entries include artifactual
sequences not
derived from the microorganism, e.g., primer sequences, host sequences, and
other
contaminants. It may be desirable to perform an initial alignment or plurality
of alignments on
a database. Regions that show irregularities in read coverage when multiple
samples are
aligned can be masked or removed as an artifact. The detection of such
irregular coverage can
be done by various metrics, such as the ratio between coverage of a specific
nucleotide and
the average coverage of the entire contig within which this nucleotide is
found. In general, a
sequence that is represented as greater than about 5X, about 10X, about 25X,
about 50X,
about 100X the average coverage of that reference sequence can be artifactual.
Alternatively,
a binomial test can be applied to provide a per-base likelihood of coverage
given the overall
coverage of the contig. Removal of contaminant sequence from reference
databases allows
accurate identification of microbes.
[00171] Each high confidence read may align to multiple organisms in the
given
microbial database. To correctly assign organism abundance based upon this
possible
mapping redundancy, an algorithm can be used to compute the most likely
organism (for
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example, see Lindner et al. Nucl. Acids Res. (2013) 41(1): el0). For example,
GRAMMy or
GASiC algorithms can be used to compute the most likely organism that a given
read came
from.
[00172] Alignments and assignment to a host sequence or to a non-host
(e.g.,
microbial) sequence may be performed in accordance with art-recognized
methods. For
example, a read of 50 nt. may be assigned as matching a given genome if there
is not more
than 1 mismatch, not more than 2 mismatches, not more than 3 mismatches, not
more than 4
mismatches, not more than 5 mismatches, etc. over the length of the read.
Publically available
algorithms may be used for alignments and identification. A non-limiting
example of such an
alignment algorithm is the bowtie2 program (Johns Hopkins University).
[00173] These assignments of reads to an organism (e.g., host organism, non-
host
organism, microbe, pathogen, etc.) can then totaled and used to compute the
estimated
number of reads assigned to each organism in a given sample, in a
determination of the
prevalence of the organism in the sample (for example, a cell-free nucleic
acid sample). This
information can be used to determine an origin of a pathogen or contaminant.
The analysis
can normalize the counts for the size of the microbial genome to provide a
calculation of
coverage for the microbe. The normalized coverage for each microbe can be
compared to the
host sequence coverage in the same sample to account for differences in
sequencing depth
between samples.
[00174] Further, a dataset of microbial organisms represented by sequences
in the
sample, and the prevalence of those microorganisms can be optionally
aggregated and
displayed for ready visualization, e.g., in the form of a report.
[00175] The present disclosure provides normalization methods. In some
cases, the
methods of the present disclosure may comprise one or more normalization
methods. The
normalization methods provided by the present disclosure allow for efficient
and improved
measurements or amounts of disease-specific, pathogen-specific, or organ-
specific nucleic
acids detected in a sample.
[00176] The normalization methods of the present disclosure generally use
spike-in
synthetic nucleic acids. The spike-in synthetic nucleic acids may be used to
normalize the
sample in a number of different ways. The spike-in nucleic acids may normalize
across all
samples and all methods of measuring disease-specific nucleic acids, pathogen-
specific
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nucleic acids or other target nucleic acids. In some cases, the spike-ins may
be used to
increase the precision of a relative abundance calculation of a pathogen
nucleic acid (or
disease-specific nucleic acid or target nucleic acid) in a sample compared to
other pathogen
nucleic acids in the sample.
[00177] In general, a known concentration (or concentrations) of species of
synthetic
nucleic acids may be spiked into each sample. In many cases, the species of
synthetic nucleic
acids can be spiked in at equimolar concentration of each species. In some
cases, the
concentrations of the species of synthetic nucleic acids can be different.
[00178] The abundance of the nucleic acid species may be altered due to the
inherent
biases of the sample handling, preparation, and measurement (e.g., detection).
After
measurement, the efficiency of recovering nucleic acids of each length can be
determined by
comparing the measured abundance of each "species" of spiked nucleic acid to
the amount
spiked in originally. This can yield a "length-based recovery profile".
[00179] The "length-based recovery profile" may be used to normalize the
abundance
of all (or most, or some) disease-specific nucleic acids, pathogen nucleic
acids, or other target
nucleic acids by normalizing the disease-specific nucleic acid abundances (or
the abundances
of the pathogen nucleic acids or other target nucleic acids) to the spiked
molecule of the
closest length, or to a function fitted to the spiked molecules of different
lengths.
[00180] This process may be applied to target nucleic acids such as the
pathogen-
specific nucleic acids, and may result in an estimate of the "original length
distribution of all
pathogen-specific nucleic acids" at the time of spiking the sample. The
"original length
distribution of all target nucleic acids" may show the length distribution
profile for the target
nucleic acids (e.g., pathogen-specific nucleic acids or organ-specific nucleic
acids) at the time
of spiking the sample. It is this length distribution that the spiked nucleic
acids can seek to
recapitulate in order to achieve perfect or near-perfect abundance
normalization. It is this
length distribution that the spiked nucleic acids can seek to recapitulate in
order to achieve
determine endogenous fragment length distribution of target nucleic acids.
[00181] As it may not be possible to spike a sample with a mixture of known
nucleic
acids that exactly recapitulates the relative abundance profile of disease-
specific nucleic acids,
pathogen nucleic acids, or other target nucleic acids in that specific sample,
in part because
the sample may have been used up or time may have changed the relative
abundance profile,
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each "species" of spike-in can be weighted in proportion to its relative
abundance within the
"original length distribution of all disease-specific nucleic acids". The sum
of all "weighting
factors" can equal 1Ø
[00182] Normalization can involve a single step or a series of steps. In
some cases, the
abundance of disease-specific nucleic acids (or pathogen nucleic acids or
other target nucleic
acids) may be normalized using the raw measurement of the closest sized spiked
nucleic acid
abundance to yield the "Normalized disease-specific nucleic acid (or pathogen
nucleic acids
or other target nucleic acid) abundance". Then, the "Normalized disease-
specific nucleic acid
abundance" (or pathogen nucleic acids or other target nucleic acid abundance)
may be
multiplied by the "weighting factor" to adjust for the relative importance of
recovering that
length, yielding the "Weighted normalized disease-specific (or pathogen-
specific or other
target) nucleic acid abundance". One advantage of this method of normalization
may be that
it allows comparable measurements of target nucleic acid (e.g., disease-
specific nucleic acid,
pathogen nucleic acid) abundance across all (or most) methods of measuring
disease-specific
nucleic acid abundance, regardless of method.
[00183] Such assays may involve measuring the amount of target nucleic
acids (e.g.,
disease-specific nucleic acids) in biological samples (e.g., plasma) to detect
the presence of a
pathogen or identify disease states or to determine if a target nucleic acid
is sample based,
reagent based, or environmental based. The methods described herein can make
these
measurements comparable across samples, times of measurement, methods of
nucleic acid
extraction, methods of nucleic acid manipulation, methods of nucleic acid
measurement,
and/or a variety of sample handling conditions.
[00184] The present disclosure provides a diversity loss value measurement.
In some
cases, the methods of the present disclosure may comprise determining a
diversity loss value.
[00185] The number of deduplicated (e.g., removed replicates) SPANK
molecules
detected in a particular library is a proxy for the minimum concentration
detectable in that
library. This can be useful for setting a threshold based on minimum
concentration of the
SPANK molecules detectable in that library. The threshold can be useful to
ensure sufficient
sequencing depth for detection of pathogen. The threshold can also be useful
in making sure
that pathogen signal was not due to cross contamination from other samples.
For example,
enrichment of pathogens relative to the threshold set by the SPANK molecules
can be
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compared between different samples. More generally, it is proportional to the
efficiency with
which that library converted DNA molecules in the original sample to reads in
the DNA
sequencing data
[00186] The spiked-in SPANK molecules provided by the disclosure may be
used to
calculate the diversity loss value. A diversity loss value may be determined
as shown in FIG.
5. In some cases, if the diversity of the SPANK sequences is high enough, the
SPANK
sequences spiked into a sample can be assumed to be essentially all unique.
Therefore, any
duplicate SPANK sequences that are sequenced are likely due to PCR
amplification and not
due to multiple copies of the same SPANK sequence being added into the sample
and can be
removed from the analysis. In addition, if each SPANK sequence is unique, the
total number
of SPANK sequences originally added to a sample is known based on the nucleic
acid
concentration and volume added to the sample, and the total number of unique
SPANK
sequencing reads after sequencing is known; together these values can be used
to calculate a
diversity loss value.
C. Absolute Abundance (MPM)
[00187] The present disclosure provides an absolute abundance measurement
(also
referred to as "molecules per microliter" (MPMs)).
[00188] Generally, the absolute abundance of a target nucleic acid in a
sample (e.g.,
DNA or RNA), may be determined by normalizing the number of sequence reads of
a target
nucleic acid with the empirically determined diversity loss value.
[00189] In some cases, an absolute abundance measurement may comprise
spiking the
sample with nucleic acids of various lengths or a single length and at known
concentrations.
In some cases, the fraction of information from the sample that is actually
observed in the
sequencing data can be observed for each spike-in length (e.g., by comparing
observed reads
with reads associated with the spiked nucleic acids, or by dividing the
observed reads by the
spike reads). The original numbers of non-host or pathogen molecules at each
length can be
back-calculated as well (e.g., inferred in part from the number of spike-in
reads at each
length). This load can be converted into a "molecules per microliter"
measurement.
[00190] In many cases, the methods for detecting molecules per microliter
(as well as
other methods provided herein) may involve removal or sequestration of low-
quality reads.
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Removal of low-quality reads may improve the accuracy and reliability of the
methods
provided herein. In some cases, the method may comprise removal or
sequestration of (in any
combination): un-mappable reads, reads resulting from PCR duplicates, low-
quality reads,
adapter dimer reads, sequencing adapter reads, non-unique mapped reads, and/or
reads
mapping to an uninformative sequence.
[00191] In some cases, the sequence reads can be mapped to a reference
genome, and
the reads not mapped to such reference genome can be mapped to the target or
pathogen
genome or genomes. The reads, in some instances, may be mapped to a human
reference
genome (e.g., hg19), while remaining reads are mapped to a curated reference
database of
viral, bacterial, fungal, and other eukaryotic pathogens (e.g., fungi,
protozoa, parasites).
[00192] The present disclosure provides various control and references,
which may be
used to determine if a measurement provided by the present disclosure
indicates that the
subject has an infection at a certain infection stage or at a site of
localization.
[00193] Often, the methods comprise processing a reference or a control
using the
methods of the present disclosure. In some cases, the control or reference
values may be
measured as a concentration or as a number of sequencing reads. The level may
be a
qualitative or a quantitative level. Based on sequence reads from the control
or reference
samples, a baseline level of the target nucleic acid (e.g., pathogen species,
genetic variants,
contaminants introduced from the laboratory environment, or organ-derived) may
be
determined.
[00194] In some cases, the control or reference values may be pathogen-
dependent. For
example, a control value for H. pylon may be different than a control value
for Clostridium
difficile. A database of levels or control values may be generated based on
samples obtained
from one or more subjects, for one or more pathogens, and/or for one or more
time points.
Such a database may be curated or proprietary.
[00195] In some cases, the control or reference value is a predetermined
absolute value
indicating the presence or absence of the cell-free pathogen nucleic acids or
cell-free organ-
derived nucleic acids. The control or reference value may be a value obtained
by analyzing
cell-free nucleic acid levels of a subject without an infection. In some
cases, the control or
reference value may be a positive control value and may be obtained by
analyzing cell-free
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nucleic acids from a subject with a particular known infection, or with a
particular known
infection of a specific organ.
[00196] In some cases, a control can include identification of a set of
commensal
microorganisms or natural microflora that are or are not causative of an
infection using
control samples from healthy individuals. A threshold can be set based on the
set of
commensal microorganisms in control samples.
[00197] A Poisson model or other statistical model may be used to determine
whether
the determined baseline level of the clinical sample is significantly higher
than reference
control. Where the sequence reads from a clinical sample is significantly
higher than the
reference control this indicates that the read is informative. In some cases,
such informative
reads can be selected for determining a threshold for two different clinical
groups.
[00198] Depending on the target nucleic acid and the level of background
observed
across the samples it may be desirable to subtract or filter out sequence
reads using one or
more references. Filtering can be done in combination with selecting, and
before or after
selecting. In some embodiments, the at least one reference value is based on
levels of the
pathogen nucleic acids detected in one or more samples selected from the group
consisting of
water sample, blood sample, plasma sample, serum sample, urine sample, body
fluid sample,
reagent sample, sample from a healthy subject or any combination thereof.
[00199] The control value may be a level of cell-free pathogen or cell-free
organ-
specific nucleic acids obtained from the subject at a different time point.
[00200] In some cases, a sample may be taken at a time point prior to a
later test time
point (e.g., after therapeutic intervention, or after a certain time has
lapsed for watchful
waiting). In such cases, comparison of the level at different time points may
indicate the
presence of infection, presence of infection in a particular organ, improved
infection, or
worsening infection. For example, an increase of pathogen or organ-specific
cell-free nucleic
acids by a certain amount over time may indicate the presence of infection or
of a worsening
infection, e.g., an increase of at least 5%, 10%, 20%, 25%, 30%, 50%, 75%,
100%, 200%,
300%, or 400% compared to an original value may indicate the presence of
infection, or of a
worsening infection. In other examples, a reduction of pathogen or organ-
specific cell-free
nucleic acids by at least 5%, 10%, 20%, 25%, 30%, 50%, 75%, 100%, 200%, 300%,
or 400%
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compared to an original value may indicate the absence of infection, or of an
improved
infection (e.g., eradication of an infection).
[00201] Samples may be taken over a particular time period, such as every
day, every
other day, weekly, every other week, monthly, or every other month. For
example, an
increase of pathogen or organ cell-free nucleic acids of at least 50% over a
week may indicate
the presence of infection.
[00202] The methods may comprise determining a threshold value or range of
values.
A threshold can be used to identify samples that are in a certain clinical
group (colonized
stage vs. invasive disease stage or no organ infection vs. infected organ). A
threshold can be
used to identify or select sequence reads that are informative from a clinical
sample.
Generally, a desirable threshold will be one that maximizes the number of true
positives,
while minimizing the number of false positives. In some cases, the threshold
may be selected
using a ROC curve analysis. In some cases, the threshold may be selected based
on
performance metrics.
Threshold Selection
[00203] A threshold may be selected based on its performance by using
various
statistical methods such as, Receiver Operating Characteristic (ROC) curve
analysis. ROC
analysis may be used to assess the performance of the classifier over its
entire operating range
before selecting a cut-off threshold value. To determine which threshold cut-
off should
performs the best using a ROC curve one can move the threshold progressively
across the
range (e.g., from 0 to 1.0) to find a cut-off the results in decreasing the
number of false
positives and increasing the number of true positives.
[00204] ROC analysis may be conducted by plotting the data obtained from
the
methods of the present disclosure as follows: TP (sensitivity) against FP (1 ¨
specificity).
Using the ROC graph, a perfect or near perfect classifier will generally go
straight up the Y-
axis and then along the X-axis, while a classifier with no power to classify
the samples in
different clinical groups will generally sit on the diagonal. Most classifiers
will fall
somewhere in between these two extreme cases and user can pick a threshold
based on its best
possible or desired performance.
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[00205] Performance metrics such as accuracy, sensitivity, specificity,
positive
predictive value, or negative predictive value can be used to select a
threshold. In some cases,
one performance metric can be used to select a threshold. In some cases,
multiple
performance metrics can be used to select a threshold.
[00206] Any threshold applied to a dataset (in which PP is the positive
population and
NP is the negative population) is going to produce true positives (TP), false
positives (FP),
true negatives (TN) and false negatives (FN).
[00207] In some cases, the accuracy performance metric can be used to
determine the
probability of a correct classification. Accuracy may be calculated by
applying the following
equation: (TP + TN)/(PP + NP). In some cases, the accuracy is calculated using
a trained
algorithm.
[00208] In some cases, the sensitivity performance metric can be used to
determine the
ability of the test to detect disease in a population of diseased individuals.
The percent
sensitivity may be calculated by applying the following equation: TP/(TP +
FN).
[00209] In some cases, the specificity performance metric can be used to
determine the
ability of the test to correctly rule out the disease in a disease-free
population. Specificity may
be calculated by applying the following equation: TN/(TN + FP).
[00210] When classifying a sample for diagnosis of infection, there are
typically four
possible outcomes from a binary classifier. If the outcome from a prediction
is p and the
actual value is also p, then it is called a true positive (TP); however, if
the actual value is n
then it is said to be a false positive (FP). Conversely, a true negative has
occurred when both
the prediction outcome and the actual value are n, and false negative is when
the prediction
outcome is n while the actual value is p. For a test that detect a disease or
disorder such an
infection, a false positive in this case may occur when the subject tests
positive, but actually
does not have the infection. A false negative, on the other hand, may occur
when the subject
actually does have an infection but tests negative for such infection.
[00211] The positive predictive value (PPV), or precision rate, or post-
test probability
of disease, is the proportion of patients with positive test results who are
correctly diagnosed.
It may be calculated by applying the following equation: PPV= TP/(TP+FP) x
100. The PPV
may reflect the probability that a positive test reflects the underlying
condition being tested
for. Its value does however may depend on the prevalence of the disease, which
may vary.
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[00212] The Negative Predictive Value (NPV) can be calculated by the
following
equation: TN/(TN+FN) x 100. The negative predictive value may be the
proportion of
patients with negative test results who are correctly diagnosed. PPV and NPV
measurements
can be derived using appropriate disease prevalence estimates.
[00213] A threshold value may be set based on the user's desired
performance in
specificity and sensitivity to distinguish between the two clinical groups. In
some cases, a
method provided by the disclosure may have a specificity greater than 70%,
75%, 80%, 85%,
86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or
99.5%,
and a sensitivity greater than 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%,
99%,
99.5% or more.
Applications
[00214] The methods provided by the disclosure can be applied for a variety
of
purposes, such as to diagnose or detect an infection, to determine the
biological relationship
between a microbe and a host, infection stage of an infection, to predict if
the infection will
progress to an invasive disease stage, to monitor the efficacy and response to
a treatment for
infection, to modify or optimize a therapy for a better clinical response, to
stop a treatment or
therapy. Thus, using the methods provided by the disclosure one can provide
individualized
treatment to a subject that is tailored according to the data obtained by the
methods.
[00215] Pathogens that are causing infection in a subject are expected to
have several
characteristics such as, but not limited to, elevated absolute abundance
levels, abnormal
nucleic acid length distribution profiles compared to an asymptomatic
reference or a control,
or they may have both characteristics. Likewise, pathogens that are infecting
a subject's
organ are expected to have elevated absolute abundance levels, abnormal
nucleic acid length
distribution profiles compared to an asymtomatic reference or a control, or
they may have
both characteristics. Pathogens that are causing infection in a subject can
have several
characteristics such as, but not limited to nucleic acid length distribution
profiles comparable
to a symptomatic reference or a control.
A. Infection Stage
[00216] The methods provided by present disclosure may be used to detect,
diagnose,
treat, monitor, predict, or prognose the infection stage in a subject. The
pathogen causing the
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infection may be a bacterium, virus, fungus, parasite, yeast, or other
microbe, particularly an
infectious microbe. In some cases, the methods can be used to determine if the
subject is in
the colonization or invasive disease stage. In some cases, the methods can be
used to detect if
the subject is in the incubation stage, a prodromal stage, an illness stage, a
decline stage, a
convalescence stage, an eradication stage, chronic stage, or an invasive
stage. In some cases, a
method determines if an infection is active or latent stage.
[00217] The methods of the disclosure may be used in conjunction with other
medical
tests. For example, the methods can be used before or after a stool antigen
test, urea breath
test, serology, urease testing, histology, bacterial culture and sensitivity
testing, biopsy, or
endoscopy is taken from a subject. In some cases, the method described herein
is conducted
without conducting a stool antigen test, urea breath test, serology, urease
testing, histology,
bacterial culture and sensitivity testing, biopsy, or endoscopy on the
subject.
[00218] In some cases of a method described herein, the method reduces the
risk of an
infection progressing to invasive disease stage by at least 10%, at least 20%,
at least 30%, at
least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or at least
90%. In some
cases of a method described herein, the method reduces the risk of mortality
and/or morbidity
related to complications in the invasive disease stage by at least 10%, at
least 20%, at least
30%, at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, or
at least 90%.
[00219] The methods described herein may further comprise RNA sequencing
(RNA-
Seq) of cell free nucleic acids derived from the subject's organ. Tissue
damage caused by an
infection may lead to release of cell-free nucleic acids from the infected
organ or tissue into
the blood. FIG. 3 depicts an example for the release of cell-free DNA. An
increase of e.g.
cell-free RNA derived from an organ in a sample may indicate that the
subject's organ has
been infected by a pathogen.
[00220] For example, a method may comprise analyzing circulating cell-free
pathogen
nucleic acids from a pathogen associated with one or more clinical symptoms.
The method
may further comprise conducting an RNA-Seq to detect an increase in organ
derived cell-free
RNA in the subject's blood. The combination of these test results may indicate
that the
pathogen has infected the subject, as well as determine which organ is
infected in the subject.
[00221] The RNA-Seq test may be conducted contemporaneously with another
clinical
method to detect an infection, subsequent to a clinical method to detect an
infection, or prior
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to a clinical method detect and infection. In other cases, RNA-Seq may be used
independently
to investigate organ health or may provide increased confidence that an
infection detected by
another clinical method described herein is an infection of a particular
organ.
[00222] In some cases, an RNA-Seq test may be able to determine if an
infection is at
an invasive disease stage. In some cases, the RNA sequencing test may be
repeated over time
to determine whether the infection is worsening or improving in a particular
organ or tissue,
or whether it is spreading to different organ or tissue in the subject.
Likewise, the pathogen
detection assay provided herein may also be repeated over time in conjunction
with the organ
infection assay.
[00223] An RNA-Seq test (or series of RNA-Seq tests) may sometimes be
performed
after a method described herein produces a positive test result (e.g.,
detection of a pathogen
infection). The RNA-Seq test may be especially useful for confirming the
infection or for
identifying the location of the infection. For example, the methods may detect
the presence of
a pathogen in a subject by analyzing circulating cell-free nucleic acids, but
the site of
infection may be unclear. In such case, the method may further comprise
sequencing cell-free
RNA from the subject to confirm that the infection is within an organ.
Absolute Abundance of Organ-Specific RNA
[00224] In some cases, an absolute abundance level of organ-specific RNA
sequences
can be used as an indicator that an organ in the subject is infected by a
pathogen. The
detection of an organ infection may involve comparing a level of organ-
specific nucleic acids
with a control or reference value to determine the presence or absence of the
organ nucleic
acids and/or the quantity of organ-specific nucleic acids. The level may be a
qualitative or a
quantitative level.
[00225] In some cases, the control or reference value is a predetermined
absolute value
indicating the presence or absence of the cell-free organ-derived nucleic
acids. For example,
detecting a level of cell-free pathogen nucleic acids above the control value
may indicate the
presence of an infection in an organ, while a level below the control value
may indicate the
absence of an infection in an organ.
[00226] The control value may be a value obtained by analyzing cell-free
nucleic acid
levels of a subject without an infection (e.g., a healthy control). In some
cases, the control
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value may be a positive control value obtained by analyzing cell-free nucleic
acids from a
subject with a particular infection, or with a particular infection of a
specific organ.
[00227] The control or reference values may be measured as a concentration
or as a
number of sequencing reads. Control or reference values may be pathogen-
dependent, organ-
dependent or both pathogen-dependent and organ-dependent. A database of levels
or control
values may be generated based on samples obtained from one or more subjects,
for one or
more pathogens, and/or for one or more time points. Such a database may be
curated or
proprietary.
[00228] In some embodiments, the control or reference absolute abundance
value
indicates the presence or absence of a site of localization in a subject. For
example, detecting
an absolute abundance level of cell-free pathogen nucleic acids above the
control or reference
value may indicate that the infection is in an organ, while an absolute
abundance value below
the control or reference value may indicate that the infection is not in an
organ. In some cases,
detecting an absolute abundance level of cell-free pathogen nucleic acids
above the control or
reference value may indicate that the infection is in an organ, while an
absolute abundance
value below the control or reference value may indicate that the infection is
not in an organ.
Distribution of Fragment Lengths of Organ-Specific RNA
[00229] In some cases, the distribution of fragment lengths of organ-
specific RNA
sequences indicates that an organ in a subject is infected by a pathogen.
[00230] For example, detecting an abnormal distribution of cell-free organ-
specific
nucleic acids may indicate that the organ is infected, while a normal
distribution of cell-free
organ-specific nucleic acids may indicate that the organ is not infected.
[00231] The control fragment length distribution may be predetermined by
analyzing
cell-free nucleic acid levels of a subject without an infection in an organ
(e.g., a healthy
control). The control fragment length distribution may be obtained in parallel
by analyzing
cell-free nucleic acid levels in a subject having an organ infection that are
not associated with
the infection.
[00232] In some embodiments, the control or reference distribution of
fragment lengths
indicates the presence or absence of a site of localization. For example,
detecting an abnormal
distribution of cell-free pathogen nucleic acids may indicate that the
infection is in an organ,
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while a normal distribution of cell-free pathogen nucleic acids may indicate
that the infection
is not is in an organ. In some cases, detecting an abnormal distribution of
cell-free pathogen
nucleic acids may indicate that the infection is in an organ, while a normal
distribution of cell-
free pathogen nucleic acids may indicate that the infection is not in an
organ.
Threshold Value or Range of Values for Organ-Specific RNA
[00233] In some cases, a threshold cut-off can be used as an indicator that
an organ in
the subject is infected by a pathogen as provided herein. A threshold cut-off
can be
determined as provide herein by using organ-specific RNA sequences from a
subject infected
by a pathogen and comparing those to a control or reference.
[00234] In some cases, the sample is identified as having an infected organ
with an
accuracy of greater than 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%,
93%, 94%,
95%, 96%, 97%, 98%, 99%, 99.5% or more. In some cases, the sample is
identified as having
an infected organ with a sensitivity of greater than 75%, 80%, 85%, 86%, 87%,
88%, 89%,
90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 99.5% or more. In some
cases, the
sample is identified as having an infected organ with a specificity of greater
than 75%, 80%,
85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%,
99.5%
or more 95%.
[00235] In some cases, the sample is identified as having an infected organ
with a
positive predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%,
98.5%,
99%, 99.5% or more. In some cases, the sample is identified as having an
infected organ with
a negative predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%,
98%, 98.5%,
99%, 99.5% or more.
[00236] In some cases, the sample is identified as having an infected organ
with a
sensitivity of greater than 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%,
93%,
94%, 95%, 96%, 97%, 98%, 99%, 99.5% or more, and a specificity of greater than
75%, 80%,
85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%,
99.5%
or more 95%.
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B. Individualized Treatment and Monitoring
[00237] The present disclosure also provides methods for individualized
treatment for
an infected subject or a subject who is susceptible or at risk for infections
(e.g.,
immunosuppressed, immunocompromised, living conditions, or genetic variations
resulting in
increased susceptibility for infection). Individualized treatment can include
predicting if an
infection will progress to an invasive disease stage, monitoring the efficacy
of a therapy in a
subject, modifying a therapeutic regimen depending on the subject's response
to the therapy,
and determining the pathogen's resistance to a particular therapeutic.
[00238] In some cases, the methods can be used to detect, diagnose,
predict, or
prognose the pathogen's resistance to a particular therapeutic. In some cases,
the methods
may further comprise sequencing of the subject's DNA for genetic variations
that are
associated with therapeutic resistance to therapeutics or to a particular
therapeutic.
[00239] In some cases, samples may be collected serially at various times
before or
during the course of the infection to determine the pathogen's and subject's
response to a
treatment, thereby providing a regimen that is individually tailored. In some
cases, the
serially-collected samples are compared to each other to determine whether the
infection is
improving or worsening in the subject.
[00240] The treatment may involve administering a drug or other therapy to
reduce or
eliminate the colonization or invasive disease associated with the infection.
In some cases,
the subject may be treated prophylactically to prevent the development of an
infection. Any
medical procedure or treatment including administration of a drug can be used
to improve or
reduce the symptoms of an infection. Some nonlimiting exemplary drugs that can
be used are
antibiotics (such as ampicillin, sulbactam, penicillin, vancomycin,
gentamycin,
aminoglycoside, clindamycin, cephalosporin, metronidazole, timentin,
ticarcillin, clavulanic
acid, cefoxitin), antiretroviral drugs (e.g., highly active antiretroviral
therapy (HAART),
reverse transcriptase inhibitors, nucleoside/nucleotide reverse transcriptase
inhibitors
(NRTIs), Non-nucleoside RT inhibitors, and/or protease inhibitors), or
immunoglobulins.
[00241] The present disclosure also provides methods of adjusting a
therapeutic
regimen. For example, the subject may have been administered a drug to treat
the infection.
The methods provided herein may be used to track or monitor the efficacy of
the drug
treatment. In some cases, the therapeutic regimen may be adjusted, depending
on upward or
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downward course of the infection. For example, if the methods provided herein
indicate that
an infection is not improving with drug treatment, the therapeutic regimen may
be adjusted by
changing the type of drug or treatment, discontinuing the use of the drug,
continuing the use
of the drug, increasing the dose of the drug, or adding a new drug or
treatment to the subject's
therapeutic regimen.
[00242] In some cases, the therapeutic regimen may involve a particular
procedure. For
example, in some cases, the methods may indicate a need for a surgical
procedure or an
invasive diagnostic procedure such as to removing a tumor or performing a
biopsy to
determine if an organ is infected. Likewise, if the methods indicate than an
infection is
improving or resolved by a therapeutic intervention, then adjusting a
therapeutic regimen may
involve reducing or discontinuing the treatment. In other cases, no
therapeutic regimen may
be given instead "watchful waiting" or "watch and wait" approach may be used
to see if the
infection clears up without any additional medical intervention.
[00243] The methods of the disclosure may comprise detection of a pathogen
in a
subject. In some cases, the method can comprise using whole-genome sequencing
of the
sample. In some cases, the method can comprise using targeted sequencing of
the sample,
where specific primers are used to detect a particular pathogen of interest.
Often, a pathogen
can have a suggested treatment cycle. For example, the treatment cycle for H.
pylon is shown
in FIG. 6. The methods provided by the disclosure may be used at any stage in
the treatment
cycle.
[00244] The methods of the disclosure can be applied to any pathogen that
has various
stages of infection. The methods may be especially useful for pathogens that
have a
colonization stage and an invasive disease stage. In some cases, the invasive
disease stage
may be caused by the pathogen infection. In some cases, the invasive disease
stage may be
associated with the pathogen infection.
[00245] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
monitor, predict, or prevent colonization by Heliobacter pylori (H. pylon). H.
pylori
colonization can be asymptomatic. In some cases, colonization may appear as an
acute
gastritis with abdominal pain (stomach ache) or nausea. The disclosure
provides methods to
detect, monitor, diagnose, prognose, treat, or prevent invasive H. pylon
disease. Subjects
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with invasive H. pylon disease may develop complications such as, chronic
gastritis, peptic
ulcer disease, gastric adenocarcinoma, stomach cancers, and/or lymphoma.
[00246] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict, or prevent colonization by Clostridium difficile (CDI). CDI may
present as
asymptomatic or symptomatic. The clinical spectrum of a CDI infection can
range from mild-
to-moderate, severe, or complicated disease. Subjects with mild-to-moderate
CDI may present
with diarrhea, colitis, including fever, leukocytosis, and/or cramps. The
severity of CDI
abdominal and systemic symptoms may increase with the severity of the
infection. The
methods may be used to detect, monitor, diagnose, prognose, treat, or prevent
invasive CDI
disease. Subjects with complicated or invasive CDI disease may develop
pseudomembranous
colitis, toxic megacolon, perforation of the colon, and/or sepsis.
[00247] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict, or prevent colonization by Haemophilus influenza. Generally,
Haemophilus influenza
colonizes the upper respiratory tract of a subject. The disclosure provides
methods to detect,
monitor, diagnose, prognose, treat, or prevent invasive Haemophilus influenza
disease.
Subjects with invasive Haemophilus influenza disease may develop complications
such as,
sepsis and/or meningitis.
[00248] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by Salmonella. The disclosure also provides methods to
detect,
monitor, diagnose, prognose, treat, predict, or prevent invasive Salmonella
disease. Some
non-limiting examples of Salmonella serotypes that are associated with
invasive disease
include but are not limited to, Typhimurium, Typhi, Enteritidis, Heidelberg,
Dublin, Paratyphi
A, Choleraesuis, and Schwarzengrund. Subjects with invasive Salmonella disease
may
develop bacteremia, meningitis, enteric fever and/or invasive non-typhoidal
Salmonella
(iNTS) disease.
[00249] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict, or prevent colonization by Streptococcus pneumoniae. The disclosure
also provides
methods to detect, monitor, diagnose, prognose, treat, or prevent invasive
Streptococcus
pneumoniae disease. Subjects with invasive pneumococcal disease may develop
bacteremia
and/or meningitis.
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[00250] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by Cytomegalovirus (CMV). Subjects infected with CMV
may have
no symptoms as the virus can cycle to dormant periods. The disclosure also
provides methods
to detect, monitor, diagnose, prognose, treat, predict, or prevent invasive
CMV disease.
Subjects with invasive CMV disease may develop complications in their eyes,
lungs, and/or
digestive system.
[00251] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict or prevent colonization by Human Papilloma virus (HPV). Subjects with
colonization
by HPV may present as non-invasive cervical intraepithelial neoplasms and/or
genital warts.
The present disclosure also provides methods to detect, monitor, diagnose,
prognose, treat, or
prevent invasive HPV disease. Subjects with invasive HPV disease may develop
cervical
cancer, anal squamous cell carcinoma, and/or anal carcinoma in situ.
[00252] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict, or prevent colonization by Epstein¨Ban virus (EBV). Subjects
colonized with EBV
may be asymptomatic or present with fatigue, fever, inflamed throat, swollen
lymph nodes in
the neck, enlarged spleen, swollen liver, and/or a rash. The disclosure also
provides methods
to detect, monitor, diagnose, prognose, treat, predict, or prevent invasive
EBV disease.
Subjects with invasive EBV disease may develop infectious mononucleosis (e.g.,
glandular
fever), have a higher risk of certain autoimmune diseases, develop cancers
such as, Hodgkin's
lymphoma, Burkitt's lymphoma, gastric cancer, nasopharyngeal carcinoma, hairy
leukoplakia,
and/or central nervous system lymphomas.
[00253] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict or prevent colonization by hepatitis B (HBV). HBV infections can be
transient or
chronic. The disclosure also provides methods to detect, monitor, diagnose,
prognose, treat,
predict, or prevent invasive disease associated with an HBV infection.
Subjects with invasive
HBV disease may develop cirrhosis, hepatocellular carcinoma, liver infection,
and/or liver
failure.
[00254] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict or prevent colonization by hepatitis C virus (HCV). HCV infection can
be acute or
chronic. Often, HCV colonization can be asymptomatic. When signs and symptoms
are
present, they may include jaundice, along with fatigue, nausea, fever and
muscle aches. Some
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subjects may have spontaneous viral clearance where others may progress to a
chronic stage.
However, where an HCV infection becomes chronic it may result in invasive HCV
disease.
The disclosure also provides methods to detect, monitor, diagnose, prognose,
treat, predict, or
prevent invasive HCV disease. Subjects with an invasive HCV disease may
develop cirrhosis,
hepatocellular carcinoma, liver infection, and/or liver failure.
[00255] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
predict or prevent colonization by human T-cell lymphoma virus 1 (HTLV-1).
HTLV-1
infects the T cells of a subject. Subjects infected with HTLV-1 may be
asymptomatic for
years. The disclosure also provides methods to detect, monitor, diagnose,
prognose, treat,
predict, or prevent invasive HTLV-1 disease. Subjects with an invasive HTLV-1
disease may
develop cancer of the T-cell (ATL) leukemia, HTLV-1 associated
myelopathy/tropical spastic
paraparesis (HAM/TSP), or other conditions.
[00256] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by gonorrhea. Subjects with a colonization infection
may have no
symptoms, while others may present with symptoms such as, burning with
urination,
testicular or pelvic pain, and/or discharge from the genitals. The disclosure
provides methods
to detect, monitor, diagnose, prognose, treat, predict, or prevent invasive
gonorrhea disease.
Subjects with invasive gonorrhea disease may develop skin lesions, joint
infection (e.g., pain
and swelling in the joints), endocarditis, and/or meningitis.
[00257] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by Syphilis. A syphilis infection can be divided into
a primary,
secondary, latent, and tertiary stages. A subject with primary stage may
present with a sore. A
subject with secondary stage may present with a skin rash, swollen lymph
nodes, and/or a
fever. During the latent or invisible stage of syphilis infection subjects are
generally
asymptomatic. The disclosure also provides methods to detect, monitor,
diagnose, prognose,
treat, predict, or prevent invasive syphilis disease. A subject with tertiary
stage or invasive
disease may develop complications in other organ systems including but not
limited to the
heart, blood vessels, brain, and/or nervous system.
[00258] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by trichomoniasis. Subjects with a colonization
infection may be
asymptomatic or they may develop inflammation in their genital area. The
present disclosure
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also provides methods to detect, monitor, diagnose, prognose, treat, predict,
or prevent
invasive trichomoniasis disease. Subjects with invasive trichomoniasis disease
may develop
cervical cancer and/or prostate cancer.
[00259] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by human herpesvirus 8 (HHV-8), is also known as
Kaposi sarcoma-
associated herpesvirus, or KSHV. Healthy subjects with a colonization
infection are
generally asymptomatic. However, subjects with weakened immune systems may
develop
invasive HHV-8 disease. The disclosure also provides methods to detect,
monitor, diagnose,
prognose, treat, predict, or prevent invasive HHV-8 disease. Subjects with
invasive HHV-8
disease may develop Kaposi sarcoma and/or several lymphoproliferative
disorders such as,
primary effusion lymphoma, multicentric Castleman disease, or B-cell lymphoma.
[00260] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by Merkel cell polyomavirus. Subjects with a
colonization infection
may be asymptomatic. The disclosure also provides methods to detect, monitor,
diagnose,
prognose, treat, predict, or prevent invasive Merkel cell polyomavirus
disease. Subjects with
invasive Merkel cell polyomavirus disease may develop Merkel cell carcinoma
(MCC)
tumors, a rare but aggressive form of skin cancer.
[00261] The disclosure provides methods to detect, monitor, diagnose,
prognose, treat,
or prevent colonization by Chlamydia. Subjects with a colonization infection
may be
asymptomatic or they may present with burning sensation when urinating or
discharge from
their genitals. The disclosure also provides methods to detect, monitor,
diagnose, prognose,
treat, predict, or prevent invasive Chlamydia disease. Untreated chlamydia can
progress to
invasive disease stage spreading to the uterus and/or fallopian tubes in
female subjects.
Subjects with invasive chlamydia disease may develop pelvic inflammatory
disease (PID),
which can result in long-term pelvic pain, inability to get pregnant, and
ectopic pregnancy.
[00262] In some cases, the subject is infected or at risk of infection by a
pathogen that
has different infection stages, such as colonization stage, and an invasive
disease stage. A
colonized subject may have no clinical signs or symptoms. In other cases, a
colonized subject
may have clinical signs or symptoms. A subject with an invasive disease can
present with
clinical signs or symptoms. In other cases, a subject with an invasive disease
can present with
no clinical signs or symptoms.
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[00263] The subject may have or be at risk of having another disease or
disorder. For
example, the subject may have, be at risk of having, or be suspected of having
cancer (e.g.,
breast cancer, lung cancer, stomach cancer, hematological cancer, etc.).
[00264] In some cases, the subject can have increased risk factors for
contracting an
infectious disease or progressing to an invasive disease stage. In some cases,
the risk factors
are associated with living conditions. Some non-limiting examples of risk
factors associated
with living conditions include, but are not limited to, crowded living
conditions, no reliable
source of clean water, living or visiting a developing country, and/or
cohabitating with
infected people.
[00265] In some cases, the risk factors for contracting an infection or for
progression to
an invasive disease are genetic variants in the subject's genomic DNA. Genetic
variants that
can be risk factors for infection include but are not limited to, single-
nucleotide
polymorphisms, deletions, insertions, or the like. In some other cases,
subjects can have
family history of disease such as gastric cancer, family history of
lymphocytic gastritis,
hyperplastic gastric polyps, or hyperemesis gravidarum.
[00266] The subject may have or be at risk of having another disease or co-
infection by
more than one pathogen. In some cases, the subject is immunosuppressed (e.g.,
organ
transplant patients). In some cases, the subject is immunocompromised (e.g.,
by
chemotherapy treatment, immune deficiency caused by AIDS, or general illness
such as
diabetes or lymphoma).
[00267] In some cases, the subject may present with one or more clinical
symptoms.
Non-limiting examples of clinical symptoms can include aching or burning pain
in the
abdomen, abdominal pain that worsens when the stomach is empty, nausea, loss
of appetite,
frequent burping, bloating in the stomach area, weight loss, severe or
persistent abdominal
pain, difficulty swallowing, bloody or black tarry stools, and/or bloody or
black vomit.
Additional clinical symptoms are known in the art.
[00268] In some cases, the subject can present with a clinical pathology
such as
atrophic gastritis, acute or chronic gastritis, hyperacidity, antigenic
stimulation, active peptic
ulcer disease, a past history of PUD, low-grade gastric mucosa-associated
lymphoid tissue
lymphoma, a history of endoscopic resection of early gastric cancer,
dyspepsia, Barrett's
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esophagus, functional dyspepsia, unexplained iron deficiency, or idiopathic
thrombocytopenic
purpura (ITP).
[00269] The subject may be infected by a pathogen or microorganism of any
type,
including bacterial, viral, fungal, parasitic, prokaryotic, eukaryotic, etc.
In some cases, the
pathogen is known, while in other cases it may be a known commensal.
[00270] In some cases, the subject may have an active or latent infection.
In some
cases, the subject is infected, but the infection is below the level of
diagnostic sensitivity of
other tests previously conducted on the subject. In some cases, the subject is
infected but
asymptomatic or the infection is at a sub-clinical level.
[00271] In some cases, the subject may have been previously treated or may
be treated
with a drug such as an antimicrobial, antibacterial, antiviral, and/or
antiparasitic drug or a
medical procedure. In some cases, the subject may have not had biopsy,
endoscopy,
colonoscopy, blood culture, or other such procedure prior to the use of the
methods herein. In
some cases, the subject may have or may have had a stool antigen test, urea
breath test,
serology, urease testing, histology, bacterial culture and sensitivity
testing, biopsy, or
endoscopy prior to the use of the methods herein.
[00272] The present disclosure provides methods for determining the
infection stage or
site in a subject using the nucleic acids obtained from a clinical sample
(e.g., blood, serum,
cells, or tissue). In some embodiments, the method comprises making a spiked-
sample by
adding synthetic nucleic acids provided by the disclosure; extracting the
nucleic acids from
the spiked-sample; generating a spiked-sample library; enriching the spiked-
sample library for
a target nucleic acid of interest; conducting a sequencing assay to obtain
sequence reads from
the spiked-sample library; and determining a measurement from the detected
nucleic acids
(e.g., DNA, RNA cell-free DNA or cell-free RNA), and comparing this
measurement to a
control or a reference to determine the infection stage or a site of
localization (e.g., organ, or
type tissue) in a subject.
[00273] Embodiments of the methods may comprise extracting nucleic acids or
target
nucleic acids from the sample or purification of nucleic acids or target
nucleic acid from
unwanted components in a reaction mixture (e.g., ligation, amplification,
restriction enzyme,
end repair, etc). Any means of extracting nucleic acids known in the art may
be used in the
methods of the application.
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[00274] The extraction can comprise separating the nucleic acids from other
cellular
components and contaminants that may be present in the sample. Nucleic acids
can be
extracted from a sample using liquid extraction (e.g., Trizol, DNAzol)
techniques. In some
cases, the extraction is performed by phenol chloroform extraction or
precipitation by organic
solvents (e.g., ethanol, or isopropanol). In some cases, the extraction is
performed using
nucleic acid-binding columns.
[00275] In some cases, the extraction is performed using commercially
available kits
such as the Qiagen Qiamp Circulating Nucleic Acid Kit Qiagen Qubit dsDNA HS
Assay kit,
Agilentim DNA 1000 kit, TruSeqTm Sequencing Library Preparation, QIAamp
Circulating
Nucleic Acid Kit, Qiagen DNeasy kit, QIAamp kit, Qiagen Midi kit, QIAprep spin
kit,) or
nucleic acid-binding spin columns (e.g., Qiagen DNA mini-prep kit). In some
cases,
extraction of cell-free nucleic acids may involve filtration or ultra-
filtration.
[00276] Nucleic acids can be extracted or purified by the use of magnetic
beads. For
example, magnetic beads with an iron-oxide core and a surface coated with
molecules
containing a free carboxylic acid or a synthetic polymer can be used. The salt
concentration or
polyalkylene glycol can be adjusted to control the strength of the bonds
between functional
groups and nucleic acid, allowing for controlled and reversible binding.
Finally, nucleic acids
can be released from the magnetic particles with an elution buffer. In some
cases, the
extraction or purification is performed using commercially available kits such
as Omega Bio-
tek Mag-Bind magnetic bead kit, Agencourt@, RNAClean@, and/or XP magnetic
beads.
[00277] The method may comprise purifying the target nucleic acids.
Purification may
be performed where a user desires to isolate the target nucleic acid from
unwanted
components in a reaction mixture. Nonlimiting exemplary purification methods
include
ethanol precipitation, isopropanol precipitation, phenol chloroform
purification, and column
purification (e.g., affinity-based column purification), dialysis, filtration,
or ultrafiltration.
[00278] Methods of generating nucleic acid libraries are known in the art.
Computer Control Systems
[00279] The present disclosure provides computer control systems that are
programmed
to implement methods of the disclosure. FIG. 7 shows a computer system 201
that is
programmed or otherwise configured to implement methods of the present
disclosure.
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[00280] The computer system 201 includes a central processing unit (CPU,
also
"processor" and "computer processor" herein) 205, which can be a single core
or multi core
processor, or a plurality of processors for parallel processing. The computer
system 201 also
includes memory or memory location 210 (e.g., random-access memory, read-only
memory,
flash memory), electronic storage unit 215 (e.g., hard disk), communication
interface 220
(e.g., network adapter) for communicating with one or more other systems, and
peripheral
devices 225, such as cache, other memory, data storage and/or electronic
display adapters.
The memory 210, storage unit 215, interface 220 and peripheral devices 225 are
in
communication with the CPU 205 through a communication bus (solid lines), such
as a
motherboard. The storage unit 215 can be a data storage unit (or data
repository) for storing
data. The computer system 201 can be operatively coupled to a computer network
("network") 230 with the aid of the communication interface 220. The network
230 can be
the Internet, an internet and/or extranet, or an intranet and/or extranet that
is in
communication with the Internet. The network 230 in some cases is a
telecommunication
and/or data network. The network 230 can include one or more computer servers,
which can
enable distributed computing, such as cloud computing. The network 230, in
some cases with
the aid of the computer system 201, can implement a peer-to-peer network,
which may enable
devices coupled to the computer system 201 to behave as a client or a server.
[00281] The CPU 205 can execute a sequence of machine-readable
instructions, which
can be embodied in a program or software. The instructions may be stored in a
memory
location, such as the memory 210. The instructions can be directed to the CPU
205, which
can subsequently program or otherwise configure the CPU 205 to implement
methods of the
present disclosure. Examples of operations performed by the CPU 205 can
include fetch,
decode, execute, and writeback.
[00282] The CPU 205 can be part of a circuit, such as an integrated
circuit. One or
more other components of the system 201 can be included in the circuit. In
some cases, the
circuit is an application specific integrated circuit (ASIC).
[00283] The storage unit 215 can store files, such as drivers, libraries
and saved
programs. The storage unit 215 can store user data, e.g., user preferences and
user programs.
The computer system 201 in some cases can include one or more additional data
storage units
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that are external to the computer system 201, such as located on a remote
server that is in
communication with the computer system 201 through an intranet or the
Internet.
[00284] The computer system 201 can communicate with one or more remote
computer
systems through the network 230. For instance, the computer system 201 can
communicate
with a remote computer system of a user (e.g., healthcare provider). Examples
of remote
computer systems include personal computers (e.g., portable PC), slate or
tablet PC's (e.g.,
Apple iPad, Samsung Galaxy Tab), telephones, Smart phones (e.g., Apple
iPhone,
Android-enabled device, Blackberry ), or personal digital assistants. The user
can access the
computer system 201 via the network 230.
[00285] Methods as described herein can be implemented by way of machine
(e.g.,
computer processor) executable code stored on an electronic storage location
of the computer
system 201, such as, for example, on the memory 210 or electronic storage unit
215. The
machine executable or machine readable code can be provided in the form of
software.
During use, the code can be executed by the processor 205. In some cases, the
code can be
retrieved from the storage unit 215 and stored on the memory 210 for ready
access by the
processor 205. In some situations, the electronic storage unit 215 can be
precluded, and
machine-executable instructions are stored on memory 210.
[00286] The code can be pre-compiled and configured for use with a machine
having a
processer adapted to execute the code or can be compiled during runtime. The
code can be
supplied in a programming language that can be selected to enable the code to
execute in a
pre-compiled or as-compiled fashion.
[00287] Aspects of the systems and methods provided herein, such as the
computer
system 201, can be embodied in programming. Various aspects of the technology
may be
thought of as "products" or "articles of manufacture" typically in the form of
machine (or
processor) executable code and/or associated data that is carried on or
embodied in a type of
machine readable medium. Machine-executable code can be stored on an
electronic storage
unit, such as memory (e.g., read-only memory, random-access memory, flash
memory) or a
hard disk. "Storage" type media can include any or all of the tangible memory
of the
computers, processors or the like, or associated modules thereof, such as
various
semiconductor memories, tape drives, disk drives and the like, which may
provide non-
transitory storage at any time for the software programming. All or portions
of the software
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may at times be communicated through the Internet or various other
telecommunication
networks. Such communications, for example, may enable loading of the software
from one
computer or processor into another, for example, from a management server or
host computer
into the computer platform of an application server. Thus, another type of
media that may
bear the software elements includes optical, electrical and electromagnetic
waves, such as
used across physical interfaces between local devices, through wired and
optical landline
networks and over various air-links. The physical elements that carry such
waves, such as
wired or wireless links, optical links or the like, also may be considered as
media bearing the
software. As used herein, unless restricted to non-transitory, tangible
"storage" media, terms
such as computer or machine "readable medium" refer to any medium that
participates in
providing instructions to a processor for execution.
[00288] Hence, a machine readable medium, such as computer-executable code,
may
take many forms, including but not limited to, a tangible storage medium, a
carrier wave
medium or physical transmission medium. Non-volatile storage media include,
for example,
optical or magnetic disks, such as any of the storage devices in any
computer(s) or the like,
such as may be used to implement the databases, etc. shown in the drawings.
Volatile storage
media include dynamic memory, such as main memory of such a computer platform.
Tangible transmission media include coaxial cables; copper wire and fiber
optics, including
the wires that comprise a bus within a computer system. Carrier-wave
transmission media
may take the form of electric or electromagnetic signals, or acoustic or light
waves such as
those generated during radio frequency (RF) and infrared (IR) data
communications.
Common forms of computer-readable media therefore include for example: a
floppy disk, a
flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM,
DVD or
DVD-ROM, any other optical medium, punch cards paper tape, any other physical
storage
medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM,
any other memory chip or cartridge, a carrier wave transporting data or
instructions, cables or
links transporting such a carrier wave, or any other medium from which a
computer may read
programming code and/or data. Many of these forms of computer readable media
may be
involved in carrying one or more sequences of one or more instructions to a
processor for
execution.
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[00289] The computer system 201 can include or be in communication with an
electronic display 235 that comprises a user interface (UI) 240 for providing,
an output of a
report, which may include a diagnosis of a subject or a therapeutic
intervention for the
subject. Examples of UI's include, without limitation, a graphical user
interface (GUI) and
web-based user interface. The analysis can be provided as a report. The report
may be
provided to a subject, a health care professional, a lab-worker, or other
individual.
[00290] Methods and systems of the present disclosure can be implemented by
way of
one or more algorithms. An algorithm can be implemented by way of software
upon
execution by the central processing unit 205. The algorithm can, for example,
facilitate the
enrichment, sequencing and/or detection of pathogen nucleic acids.
[00291] Information about a can be entered into a computer system, for
example, a
patient identifier such as information about infection stage or risk of
infection, patient
background, patient medical history, previous infections, or ultrasound scans.
Patient
identifiers can be separated from clinical samples to obtain de-identified
samples, for example
by the sample sender or the sample recipient. Patient identifiers can be
replaced with
accession numbers or other non-individual identifying code. Clinical samples
can be
sequenced using a high-throughput sequencer. De-identified sample sequence
data generated
by sequencer can be uploaded to a server, such as a cloud server. Using
methods disclosed
herein, pathogen nucleic acids within de-identified samples can be detected to
obtain de-
identified result data. De-identified result data can be downloaded from the
server. The de-
identified result data can be associated with patient identifiers, for example
by the sample
sender or the sample recipient.
[00292] An electronic report can be generated to indicate the infection
stage of a
pathogen. An electronic report can be generated to indicate prognosis. An
electronic report
can be generated to indicate diagnosis. If an electronic report indicates
there is a treatable
infection, the electronic report can be generated to prescribe a therapeutic
regimen or a
treatment plan. The computer system can be used to analyze results from a
method described
herein, report results to a patient or doctor, or come up with a treatment
plan.
Kits
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[00293] Also provided are reagents and kits thereof for practicing one or
more of the
methods described herein. The subject reagents and kits thereof may vary
greatly. Reagents of
interest include reagents specifically designed for use in identification,
detection, and/or
quantitation of one or more pathogen nucleic acids in a sample obtained from a
subject
infected with a pathogen or at risk of infection.
[00294] The kits may comprise reagents necessary to perform nucleic acid
extraction
and/or nucleic acid detection using the methods described herein such as PCR
and
sequencing. The kit may further comprise a software package for data analysis,
which may
include reference profiles for comparison with the test profile from a
clinical sample, and in
particular may include reference databases. The kits may comprise reagents
such as buffers
and water.
[00295] Such kits may also include information, such as scientific
literature references,
package insert materials, clinical trial results, and/or summaries of these
and the like, which
indicate or establish the activities and/or advantages of the composition,
and/or which
describe dosing, administration, side effects, drug interactions, or other
information useful to
the health care provider. Such kits may also include instructions to access a
database. Such
information may be based on the results of various studies, for example,
studies using
experimental animals involving in vivo models and studies based on human
clinical trials.
Kits described herein can be provided, marketed and/or promoted to health
providers,
including physicians, nurses, pharmacists, formulary officials, and the like.
Kits may also, in
some embodiments, be marketed directly to the consumer.
[00296] It will be understood that the reference to the below examples is
for illustration
purposes only and do not limit the scope of the claims.
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EXAMPLES
Example 1. Distribution Shapes and Microbe Status
[00297] Processing of biological samples with a method that lacks bias or
enables
correction of the bias in the region of fragment lengths of interest allows
the measurement of
the endogenous fragment length distributions and creates the potential to use
endogenous
fragment length distribution profiles to inform the diagnostic as well as
therapeutic aspects of
a treatment. Several different clinical samples were therefore processed show
the diversity of
the fragment length distribution profiles. A direct-to-library process with no
detectable length
and GC bias within the investigated fragment length range was applied to
obtain the shapes of
the endogenous fragment length distributions.
[00298] Clinical plasma samples: 36 diagnostically positive (i.e. the
presence of
microbes confirmed with an orthogonal test, e.g.: blood culture, targeted PCR,
Karius Test)
were collected from 36 human subjects. Single-centrifugation step plasma
extraction process
from whole blood within 24 hours of sample collection was performed for each
sample, as
previously described (See, the first centrifugation step in Fan HC et al.,
PNAS 2008; 105(42):
16266-16271, which is incorporated by reference in its entirety herein,
including any
drawings), and stored at -80 C until use. Samples were then thawed, and 200
pL of each
plasma was spiked with 2 pL of Spike-in Master Mix (see below).
[00299] Positive Assay Control Samples: Two positive controls, referred to
as assay
control samples (AC) were processed for each group of 18 samples,
respectively. AC
samples were prepared from human asymptomatic plasma spiked with enzymatically
sheared
genomes of human pathogens, purchased in purified form from ATCC (American
Type
Culture Collection). Selected human pathogens were Aspergillus fumigatus,
Escherichia coli,
Pseudomonas aeruginosa, and Staphylococcus epidermidis. 10 pL of Spike-in
Master Mix
(see below) were added per 1 mL of AC sample.
[00300] Negative Control Samples: Four 500 pL negative control samples (EC)
per 18
samples were made from aqueous buffer (10 mM Tris pH 8, 0.1 mM EDTA, 0.05 v/v%
Tween-20) with 5 pL of Spiked-in Master Mix (see below) and served as controls
for
environmental contamination (i.e., microbe and pathogen nucleic acid
contamination
introduced by either the reagents, instrumentation, consumables, operators,
and/or air during
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processing). These synthetic nucleic acids were used for normalizing the
signal in the
samples in order to account for variations in sample processing.
[00301] Spike-in Master Mix: A set of process control molecules were pre-
mixed
together in a single Spike-in Master Mix, with each Spike-in Master Mix
containing a unique
"ID Spike" process control molecule, See, for example, United States patent
9,976,181.
Spike-in Master Mix contained three classes of molecules: ID Spike molecules,
SPANK
molecules, and SPARK molecules. The latter group of molecules was composed of
two
classes of SPARKs: GC dSPARKs and Long SPARKs. The molar concentration of the
ID
Spike, SPANK molecules, and long SPARK molecules in Spike-in Master Mix was
500 pM
per molecule while GC dSPARK molecules were present at 50 pM per molecule.
[00302] "ID Spike" molecules: Each sample received a unique ID Spike single-
stranded DNA molecule characterized by a 50 base pairs long unique sequence
that was not
present in any reference genome available in public databases at the time of
processing.
[00303] SPANK molecules: SPANK molecules used were a pool of single-
stranded
DNA molecules, each 50 base pairs long with identical 3'-end and 5'-end
sequences that were
not present in any reference genome available in public databases at the time
of processing.
In addition, two stretches of 8 base pairs nested between the constant 3'-end
and 5'end
sequences were present and fully degenerate within the pool. The pool of SPANK
molecules
contained 416 unique SPANK molecules. The two degenerate stretches were
separated by a
stretch of four non-degenerate bases.
[00304] SPARK molecules: A GC Spike-in Panel was a set of molecules 32, 42,
52,
and 75 nt long where 7 different sequences with GC content 20%, 30%, 40%, 50%,
60%,
70%, and 80% were included for each length. Like some of the other molecules
provided
above, GC dSPARK sequences did not occur in the available reference genomes. A
Long
SPARK sequence set was a group of 4 non-natural sequences, each with 50% GC
content and
lengths of 100 nt, 125 nt, 150 nt, and 175 nt. A complete set of SPARK
molecules contained
32 different sequences.
[00305] Library Generation: Direct-to-library generation was described in
U.S.
Provisional Application 62/770,181 filed November 21, 2018, herein
incorporated by
reference in its entirety. Here, a template-switching based method with
Proteinase K was
utilized. Briefly, 50.0 pL of each spiked sample was mixed with 20.0 pL of 10x
Terminal
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Transferase Reaction Buffer (NEB, Ipswich, MA), 5.0 pL of Proteinase K
(Sigma), 2.0 pL of
10% Tween-20 (Thermo-Fisher Scientific, Waltham, MA), 2.0 pL of 10% Triton
X100
(Thermo-Fisher Scientific, Waltham, MA) and 121.0 pL Nuclease-free water. The
mixture
was heated to 60 C for 20 minutes and 95 C for 10 minutes and placed on ice
until cool. 2.0
pL of 10 mM dATP, 2.0 pL Terminal Transferase (20 u/ pL, NEB, Ipswich, MA) and
6.0 pL
Nuclease-free water was added to prepare the A-tailing reaction which was
incubated at 37 C
for 40 mM. 300.0 pL of Lysis/Binding Buffer (Thermo-Fisher Scientific,
Waltham, MA) was
added to the reaction. The entire volume was then added to 50.0 pL of
Dynabeads oligo
(dT)25 (Thermo-Fisher Scientific, Waltham, MA), which had been washed once
with
Lysis/Binding Buffer (Thermo-Fisher Scientific, Waltham, MA). The mixture was
incubated
at 25 C and 600 RPM. The beads were then washed twice with 600.0 pL of Wash
Buffer A
(Thermo-Fisher Scientific, Waltham, MA) and twice with 300.0 pL of Wash Buffer
B
(Thermo-Fisher Scientific, Waltham, MA) before elution in 24.0 pL of elution
buffer
(Thermo-Fisher Scientific, Waltham, MA) at 80 C and 600 RPM for 3 minutes. The
entire
eluate was transferred to a new plate. 2.0 pL 1 pM Poly dT primer (IDT) and 6
pL of
SMARTScribe 1st Strand buffer (5x) (Takara, Kusatsu, Japan) was added to the
eluate and
the resulting mixture incubated at 95 C for 1 minute before placing on ice.
The Extension
and Template-switching mix was prepared by combining 4.5 pL SMARTScribe 1st
Strand
buffer (5x) (Takara, Kusatsu, Japan), 0.5 pL dNTP mix (25 mM per nucleotide,
Thermo-
Fisher Scientific, Waltham, MA), 2.0 pL SMARTScribe Reverse Transcriptase (100
u/ pL,
Takara, Kusatsu, Japan), 2.0 pL 5 pM Template-switching Oligo (TS Oligo)
(IDT), 5.0 pL of
DTT (20M, Takara, Kusatsu, Japan), and 4 pL Nuclease-free water. The resulting
reaction
mixture was incubated for 90 min at 42 C and the reaction was heat denatured
at 70 C for 15
min. Next, 50.0 pL of NEBNext Ultra II Q5 (NEB, Ipswich, MA), and 8.0 pL of
indexing
primer mixture (NEB, Ipswich, MA) were added to the reaction from the previous
step. Amplification of the nucleic acids was performed then using the
following temperature
cycling program: 98 C for 30 seconds, 8 cycles of 98 C for 10 seconds, 65 C
for 75 seconds,
and a final extension of 65 C for 5 min. Final nucleic acid libraries were
then pooled in
groups of four ECs, two ACs, and eighteen clinical samples before using
RNAcleanTM
Ampure beads to purify the pool as described above. After purification, the
concentration of
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the nucleic acids in the library pools was measured with TapeStation as
described above and
loaded on the sequencer according to the manufacturer's recommendations.
[00306]
Sequencing: The samples were sequenced to obtain sequence reads using a
NextSeqTM 500 sequencer by Illumina. Sequencing was conducted following the
manufacturer's instructions using 76 cycles.
[00307]
Sequencing data analysis: Primary sequencing output was demultiplexed by
bc12fastq v2.17.1.14 (with default parameters), followed by the removal of the
template
switching oligos using Cutadapt. The poly A tail was removed and reads were
quality
trimmed and subsequently filtered if shorter than 20 bases by Trimmomatic v
0.32. Reads
that passed these filters were aligned against human and synthetic (including
process control
molecules and sequencing adapter) references using Bowtie v2.2.4. Reads with
alignments to
either were set aside. Reads potentially representing human satellite DNA were
also filtered
via a k-mer based method. The remaining reads were aligned against a
microorganism
reference database using BLAST v2.2.30. Reads with alignments that exhibited
both high
percent identity and high query coverage were retained, with the exception of
reads that
aligned against any mitochondrial or plasmid reference sequences. PCR
duplicates were
removed based on their alignments. Relative abundances were assigned to each
taxon in a
sample based on the sequencing reads and their alignments. For each
combination of read
and taxon, a read-sequence probability was defined that accounted for the
divergence between
the microorganism present in the sample and reference assemblies in the
database. A mixture
model was used to assign a likelihood to the complete collection of sequencing
reads that
included the read sequence probabilities and the (unknown) abundances of each
taxon in the
sample. An expectation-maximization algorithm was applied to compute the
maximum
likelihood estimate of each taxon abundance. From these abundances, the number
of reads
arising from each taxon were aggregated up the taxonomic tree. A set of
libraries may be
prepared from the respective Negative Control Buffers and processed and
sequenced within
each batch. Estimated taxon abundances from the negative control samples
within the batch
may be combined to parameterize a model of read abundance arising from the
environment
with variations driven by counting noise. Statistical significance values may
be computed for
each estimated taxon abundance and those within the CRR at high significance
levels
comprised candidate calls (i.e., significant calls). Final calls (i.e.,
reportable calls) was made
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after additional filtering was applied, accounting for read location
uniformity, read percent
identity, and cross-reactivity originating from higher abundance calls. The
number of reads of
multiple fragment lengths for each reportable microbe within each processed
nucleic acid
library was determined, the fragment length distributions were evaluated and
the fragment
length characteristic of distribution shape was determined. FIG. 8 shows
examples of some of
the distinct fragment length distribution shapes observed among the detected
microbes within
the tested clinical samples. The range of fragment lengths shown is limited to
22 bp on the
shorter end due to the minimum mapping length and 68 bp set by a combination
maximum
read length in the described sequencing experiment and adapter trimming
algorithm.
Consequently, the fragments longer than 68 bp contributed to the count in the
68 bp length
bin. The three microbes detected in the three examples shown were Candida
tropicalis,
Aspergillus oryzae, and WU polyomavirus. The fragment length distribution
shapes vary
considerably between these microbes, and are not related to the particular
species or
superkingdom as shown by the remainder of the data (not shown).
[00308] Candida tropicalis was detected in three different clinical samples
processed
here. The subset of reads from each sample that aligned to the Candida
tropicalis reference
genome was identified and their fragment length distributions were determined.
Results are
shown in FIG. 9 with Candida tropicalis fragment length distributions from
each of the three
samples in a separate panel. The left and middle panels show a distribution
with higher short
(<40 bp) and long (>65 bp) fraction relative to the 50 bp peak as compared to
the right panel
while they both have a clear peak at approximately 45-50 bp. The left 2 panels
are from
patients with disseminated Candida tropicalis infection, which without being
limited by
mechanism, can explain the increased amount of long and short fragments,
relative to the
peak. The different fragment length distributions may indicate a different
state of disease or
condition. WU polyomavirus was another example of a microbe that was detected
in multiple
clinical samples processed in this study and exhibited different fragment
length distribution in
each sample (FIG. 10). In one subject the WU polyomavirus showed only the "50
bp peak".
The second subject showed considerable contribution of the short exponential-
like fraction as
well as higher fraction of reads longer than 68 bp. While not being limited by
mechanism, the
WU polyomavirus may have incorporated in the human genome in this sample or
its genome
was released into the bodily fluid, which caused different fragmentation
patterns. In a total of
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36 clinical samples (see above), 60, 24, and 13 bacterial, fungal, and viral
microbes were
detected, respectively. The fragment length distributions of these microbes
vary considerably
as demonstrated by examples set above. Next,_the ratio of read counts detected
in the "50 bp
peak" peak vs. the short exponential-like region of the distribution for all
the detected
microbes or pathogens were determined. The obtained ratios were grouped by
their
superkingdom and histogram of ratios characteristic for each superkingdom was
generated.
Results from one such analysis are presented in FIG. 11. The same analysis was
performed
for the human DNA (i.e. host DNA) and human mitochondrial DNA (i.e. host
mitochondrial
DNA) as a control (FIG. 11.). The behaviour of the microbes depends on the
superkingdom
and that aspect must be accounted for when using fragment length distribution
shapes and
properties for diagnostic purposes.
Example 2. Analysis of plasma samples from a pregnant subject
[00309] Many types of non-host nucleic acids can be found in a sample
obtained from a
host. Fetal cell-free nucleic acids can be detected in maternal blood. In this
samples, plasma
samples were obtained from 15 pregnant women with consent and deidentified.
The samples
were processed and sequenced according to the ligation-based direct-to-library
method
described as Example lof U.S. Provisional Application 62/770,181 filed
November 21, 2018,
herein incorporated by reference in its entirety. Only the samples from
subjects pregnant with
a male fetus were considered in this analysis. Reads that aligned only to the
Y chromosome
were considered to be fetal. Reads were aligned to the human genome using
bowtie2. Reads
that mapped to chromosome Y were then aligned using bowtie2 to an index
created from all
human chromosomes except for Y. Any reads that aligned to this index were
discarded so that
only the reads unique to chromosome Y remained.
[00310] Fragment length distributions for maternal (dashed line) and fetal
(solid line)
cell-free nucleic acids from one individual are presented in FIG. 12. In this
example, the ratio
of fetal to maternal reads is higher in the "50 bp peak" region as compared to
the nucleosomal
fragment region (e.g. 150-200 bp region). On average, 4x higher concentration
of the fetal
fragments was observed within the "50 bp peak" region as compared to the
nucleosomal
length fragment region. The process employed here could be used to enrich for
the fetal
fraction.
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Example 3. Analysis of Microbes using Fragment Length Profiles
[00311] Nucleic acid libraries were prepared and sequenced from over 4000
cell-free
plasma samples using a validated Karius Test, an extraction-based method that
recovers
double-stranded DNA fragments in an unbiased way in respect to their length
and GC-content
within a fragment length range relevant to the cell-free nucleic acids. The
fragment length
profiles for detected microbes were generated and evaluated for 33 taxa that
were called 10 or
more time within the studied sample group. More specifically, the ratio of the
fraction of
short reads in low probability and high probability calls was evaluated.
Results from one such
experiment are presented in FIG. 13. In this experiment, the graph indicates
more of the low
probability calls have short reads than of the high probability calls. While
not being limited
by mechanism, these results may suggest clinical infections have longer
fragment length
distribution than colonizers or non-pathogenic organisms translocated in the
bloodstream
when end-repairable double-stranded cell-free DNAs are considered.
Example 4. Analysis of Site of Localization using Fragment Length Profiles
[00312] Nineteen clinical samples were obtained from subjects that were
confirmed to
be undergoing an infection as determined by positive urine (n=19) and/or blood
culture tests
(n=11). Nucleic acid libraries were prepared from these samples and sequenced
using a
validated Karius Test, an extraction-based method that recovers double-
stranded DNA
fragments in an unbiased way in respect to their length and GC-content within
a fragment
length range relevant to the cell-free nucleic acids. . In all nineteen
subjects, the blood and
urine cultures identified 19, and 11 microbes, respectively. The fragment
length distribution
profile shapes for the microbes detected by blood and urine cultures were
evaluated. Results
are shown in FIG. 14. While not being limited by mechanism, pathogen DNA
coming from
a deep tissue infection (lung, brain, etc.) may undergo different degradation
mechanisms
affecting the observed fragment length as DNA coming from a pathogen infecting
the
bloodstream.
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Example 5. Length Distribution Profile of Host Nucleic Acids and Infection
State
[00313] The fragment length distribution for the host nucleic acids can
help inform the
non-host nucleic acid signal within a host, e.g. microbial nucleic acid signal
or infection stage
of a host (e.g. asymptomatic vs. symptomatic). For example, the abundance of
the microbial
nucleic acids within a sample from a human host can vary over several orders
of magnitude
(Blauwkamp et al. (2016)). While the samples obtained from asymptomatic
individuals tend
to exhibit lower abundances of the microbial nucleic acids as compared to the
infected
individuals, the abundances measured in some asymptomatic samples may exceed
the lowest
abundances among the infected individuals (Blauwkamp et al. (2016)).
Additional properties
of the nucleic acid pool obtained from a sample may help distinguish between
different
infectious stage or biological relationship of a microbe with the host (e.g.
commensal vs.
pathogenic). Here, we tested the utility of the length distribution of the
host nucleic acids in
predicting infection state of the microbes within a plasma from a host. Our
methods enable
access to endogenous fragment length profiles with fragment lengths that
previous methods
typically did not access in an unbiased way. Our methods also enable access to
endogenous
fragment length profiles with fragment lengths that previous methods typically
either
discarded, disregarded or deemed unimportant.
[00314] Clinical plasma samples: 100 asymptomatic (collection criteria: No
active
health issues related to infection, and. passed normal blood screening tests),
85 diagnostically
positive (i.e. the presence of microbes confirmed with an orthogonal test,
e.g.: blood culture,
targeted PCR, Karius Test), and 45 diagnostically negative plasma samples were
collected
from human subjects. Single-centrifugation step plasma extraction process from
whole blood
within 24 hours of sample collection was performed for each sample, as
previously described
(See, Fan HC et al., PNAS 2008; 105(42): 16266-16271, which is incorporated by
reference
in its entirety herein, including any drawings), and stored at -80 C until
use. Samples were
then thawed, and 500 pL of each plasma was spiked with 5 pL of Spike-in Master
Mix (see
above). If smaller volumes were obtained, a proportionally smaller volume of
Spike-in
Master Mix was added to maintain a constant concentration of the process
control molecules
in all of the initial samples and control samples.
[00315] Positive and negative control samples were prepared as described
above.
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[00316] Direct-from-plasma nucleic acid library generation and sequencing:
Direct
to library generation was described in U.S. Provisional Application 62/770,181
filed
November 21, 2018, herein incorporated by reference in its entirety. The
libraries were
prepared and sequenced as described above in Example 1.
[00317] Results: The abundances of the significant microbes present in each
sample
were determined as described above and were given in concentration units of
Molecules Per
Microliter (MPM) of plasma sample, a normalized quantity that gives the
estimated number
of unique nucleic acid fragments for an organism in 1 microliter of plasma
sample. This
calculation was derived from the number of unique or deduplicated sequences
present for
each organism normalized to the known quantity of unique synthetic spike-ins
added to
plasma sample before the start of the process (See US Pat. No. 9,976,181). FIG
9A shows the
distribution in MPM values in asymptomatic (AP) and diagnostic positive (DP)
sample types.
The lower abundance values in DP sample types overlap with the range of MPMs
observed in
the AP samples, even if only microbes that were orthogonally confirmed are
included. (DPNos
includes microbes confirmed by the Karius Test and DP
micro micro includes microbes confirmed by
culture or PCR-based methods). Additionally, if the analysis is restricted to
microbial species
that are present in the set of AP samples and also in the set of DP samples
(in this data set the
following species fit this description: Bacillus coagulans, Enterococcus
cecorum,
Enterococcus faecalis, Haemophilus influenzae, Haemophilus parainfluenzae,
Human
mastadenovirus D, Neisseria mucosa, Pediococcus acidilactici, Prevotella
intennedia,
Prevotella melaninogenica, Saccharomyces cerevisiae, Streptococcus agalactiae,
Streptococcus salivarius, Streptococcus thermophilus), abundance is still not
always higher in
the diagnostic positive group (FIG. 15B). Consequently, abundance is not
sufficient to
distinguish the infectious state of the non-microbial host.
[00318] A combination of several measurable parameters may then be used to
distinguish asymptomatic/healthy patients from patients undergoing an
infection. To this end,
a combination of MPM microbial abundances and the distribution of the length
of the nucleic
acid fragments that mapped to the host reference (i.e. human reference in this
cohort of
samples) was investigated as a potential classifier.
[00319] FIG. 15C shows an example of a typical distribution of nucleic acid
fragments
after the library generation process is completed and as measured by a
TapeStation
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instrument. Two major peaks in fragment length can be observed: (1) a
"nucleosomal" peak
(300 ¨ 450 bp range in the electropherogram), and (2) a "sub-nucleosomal" peak
(180 ¨ 280
bp range in the electropherogram). This signal is determined by the properties
of the human
(i.e. host) nucleic acids as the microbial (i.e. non-host) nucleic acids
represent a minor
fraction of the total nucleic acid population in these samples, including the
DP sample types.
The molar and mass ratio of the human fragments contributing to the two peaks
varies
between the samples and is distinctive between the AP and DP sample types
(FIG. 15D). The
vast majority of AP samples (92%) show the "nucleosomal" peak molar fraction
to be lower
than 0.4 while the same value is distributed equally over a wider range for
the DP samples (<
0.7).
[00320] MPM microbial abundances as well as the properties of the human
fragment
length distribution show overlapping values between AP and DP samples. A
combination of
the two independent measurements may help distinguish the asymptomatic calls
from the
infectious calls in an unknown sample where the infectious stage is unknown.
FIG. 15E
shows the long human reads fraction as measured from the sequencing data (all
reads
mapping to human reference longer than 65 bp after adapter trimming) and
maximum MPM
value measured in the same sample for all AP and DP samples. The region
encompassed by
the coordinates R0,3000),(0,0.4)] is populated by AP samples exclusively.
Three of the 100
AP samples fall outside of this space (arrows in FIG. 15E). The microbes
detected in these
three samples were Helicobacter pylori, Human mestadenovirus D, and Nisseria
gonorrhoeae. All three microbes are known human pathogens, though we do not
know
whether they were pathogenic in these individuals.
[00321] A comparison between microbial MPM and the properties of the human
fragment length distribution in AP and DN samples types (FIG. 15F) reveals
that none of the
DN samples fell within a typical asymptomatic range even though they were
negative
according to the orthogonal testing.
[00322] Non-microbial signal such as e.g. properties of the fragment length
distribution
for the non-microbial host nucleic acids can be utilized in identification of
asymptomatic or
non-infectious states of a subject.
[00323] The data also indicates that the asymptomatic individuals can be
identified
from data such as presented here by combining the abundances (e.g. maximum
MPM) and
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fragment length distribution parameters, even if the MPM values for the
microbes overlap
with the range that can be observed in the diagnostic positive samples. It
also suggests that an
early detection of the infection may be possible in the absence of standard
symptoms. The
region on such a two-dimensional plane that can help distinguish between
different infectious
states of individuals can be further optimized in respect to e.g. MPMs for
specific microbial
species, or kingdom as well as microbial fragment length for improved
performance of the
test.
[00324] Finally,
the normalized size distribution of fragments aligning to the human
genome (dominated by the nuclear genome), human mitochondrial genome, all
pathogens,
significant pathogens; and bacteria, eukaryotes, viruses and archaea were
computed for all
samples. To differentiate AP from DP/DN samples a classifier was trained on
the fragment
size distributions (features), in this case by using logistic regression with
L2 regularization.
Logistic regression is a linear model for classification that multiplies
features by a set of
weights prior to transformation with a logistic function. The weights are
determined using
standard numerical optimization techniques with L2 regularization providing an
additional
constraint to minimize the sum of the squares of the weights. This has the
effect of decreasing
overfitting and effects of multicollinearity in the features. The accuracy of
this model is
assessed by using the trained model to predict the probability that each
sample is
asymptomatic or symptomatic. Values > 0.5 indicate that the sample is
predicted to be
asymptomatic, values <0.5 indicate that the same is predicted to be
symptomatic.
Additionally, the trained model provides the weights (coefficients). Positive
coefficients
indicate association with asymptomatic individuals, negative coefficients with
symptomatic
ones. FIG. 16 shows the accuracy of predicting an asymptomatic and symptomatic
infection
state based training using the normalized size distribution of fragments
aligning to the human
genome (dominated by the nuclear genome), human mitochondrial genome, all
pathogens,
significant pathogens; and, bacteria, eukaryotes, viruses and archaea. The
subgroup of nucleic
acids from the library used to train the model affects the accuracy of the
model. In addition,
the subgroup of nucleic acids from the library affects the regions of the
fragment length
distribution that have a positive predictive value for either asymptomatic or
symptomatic
state. For example, the presence of long human fragments (> 60 bp) predicts a
symptomatic
state (FIG. 16A, right panel) as do short (< 30 bp) pathogenic fragments (FIG.
16C, right
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panel). On the other hand, high concentration of fragments around 50 bp
predicts an
asymptomatic state (FIG. 16A, right panel) as do long (>65 bp) pathogenic
fragments (FIG.
16C, right panel).
Example 6. Distinguishing asymptomatic patients colonized by H. pylon vs.
patients with
active H. pylon-associated inflammation
[00325] Plasma processing and DNA extraction: Plasma is extracted from
whole blood
samples within 24 hours of sample collection, as previously described (Fan HC
et al., PNAS
2008; 105(42): 16266-16271), and is stored at -80 ¨coC. When required for
analysis, plasma
samples are thawed and circulating DNA is immediately extracted from 0.5-1 ml
plasma.
[00326] Sequencing library preparation and sequencing: Sequencing libraries
are
prepared from the purified patient plasma DNA using the NEBNext DNA Library
Prep
Master Mix Set for Illumina with standard Illumina indexed adapters (purchased
from IDT)
and post-end repair purification (e.g., MagBind beads, NEBNext End Repair
Module), or
using a microfluidics-based automated library preparation platform (Mondrian
ST, Ovation
SP Ultralow library system). Libraries are characterized using the Agilent
2100 Bioanalyzer
(High sensitivity DNA kit) and quantified by qPCR.
[00327] qPCR Validation of Sequencing Results for Selected Bacterial
Targets.
Standard qPCR kits for the quantification of selected bacterial targets (e.g.,
H. pylon) are used
to validate the sequencing results for a subset of cell-free DNA samples. The
qPCR assays are
run on cfDNA extracted from --1 ml of plasma and eluted in a 100 ml Tris
buffer (50 mM [pH
8.1-8.2]). The plasma extraction and PCR experiments are performed in
different facilities.
No-template controls are run to verify that the PCR reagents are included in
every experiment.
[00328] After removing low-quality reads, reads are mapped to the human
reference
genome. Remaining reads, presumed to be microbiome-derived, are mapped to a
reference
database of target microorganism genomes. Relative abundance for each
microorganism are
calculated using a proprietary algorithm. The algorithm reports organisms that
are present at
statistically significant amounts as compared with controls. Organisms with
over-represented
sequences are reported as positive.
[00329] Quality control (QC) measures included adding an ID-spiked-in
synthetic
nucleic acid, which is a type of spike-in that is unique for each sample in a
sequencing batch,
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and other synthetic nucleic acid spike-ins ("SPANK molecules") which are
spiked in at a
constant concentration across all libraries. Thus, the number of deduplicated
SPANK
molecules detected in a particular library is a proxy for the minimum
concentration detectable
in that library. This can be useful for setting a threshold based on minimum
concentration of
the SPANK molecules detectable in that library. The threshold can be useful to
ensure
sufficient sequencing depth for detection of pathogen. The threshold can also
be useful in
making sure that pathogen signal was not due to cross contamination from other
samples. For
example, enrichment of pathogens relative to the threshold set by the SPANK
molecules can
be compared between different samples. More generally, it is proportional to
the efficiency
with which that library converted DNA molecules in the original sample to
reads in the DNA
sequencing data. The purpose of the SPANK molecules is to help establish the
relative
abundance of the pathogen molecules within the mixture represented in a
specimen, reported
as "molecules per ml" (MPM). MPM data was used to build heatmaps and
correlation plots.
Sample Purity Ratio (SPR) aims to capture how significant the number of taxon-
associated
reads is given the estimated degree of cross-contamination in the sample. In
case of failure of
deduplicated SPANK and/or SPR, the sample was re-queued and re-run once. If QC
failed
twice on the same sample, the report was "no result."
Results.
[00330] The method is able to detect H. pylon cell-free DNA in plasma
obtained from
patients with H. pylori-associated peptic ulcer disease. The method was able
to distinguish
between patients with asymptomatic H. pylon and H. pylon disease. For the
later case, the
samples were obtained from healthy (i.e., asymptomatic) and infected subjects
and analyzed
using next-generation sequencing of cell-free plasma to detect pathogen DNA
(The Karius
Testim, Karius, Redwood City, CA). In healthy volunteers, the test detected H.
pylon in 8/106
samples assayed. Some patients identified in the dataset with an H. pyloni.
asymptomatic
colonization (C) (n.1) or an H. pylon symptomatic, chronic infection (CI)
(n.7) (see, Table
1, below. H. pylon positive samples were associated with African-American or
Hispanic race
which is consistent with the epidemiology of H. pylon infection.
[00331] Table 1. Detection of H. pylon in plasma. H. pylon Infections as
Probable,
Possible or Unlikely Causes of Sepsis
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Subject H. Type of Infection Classification of Relevant Clinical
ID pylon Patient Type Infection as Characteristics
MPM Primary Etiology
of Sepsis
SFN0032 110.17 Neutropenic Acute Possible Immunocompromised
fever patient, result
adjudicated to possible
addition of H. pylori
antibiotic coverage
599074 196.83 Suspected Acute Probable
Sepsis
111629 104.65 Suspected Chronic Unlikely
Sepsis
162140 125.21 Suspected Chronic Unlikely
Sepsis
185478 38.74 Suspected Chronic Unlikely
Sepsis
562626 176.90 Suspected Chronic Unlikely
sepsis
562871 87.73 Suspected Chronic Unlikely
sepsis
564748 71.03 Suspected Chronic Unlikely
sepsis
758884 106.19 Suspected Chronic Unlikely
sepsis
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Subject H. Type of Infection Classification of Relevant Clinical
ID pylon Patient Type Infection as Characteristics
MPM Primary Etiology
of Sepsis
263403 243.29 Suspected Chronic Unlikely
sepsis
[00332] Without being limited by mechanism, cell-free nucleic acids may be
derived
from pathogens that are dead and dying. Thus, the present method is uniquely
suited to detect
organisms that are being actively cleared by the immune system. In fact, the
assay was able to
distinguish between H. pylon in the context of active inflammation rather than
asymptomatic
colonization.
Example 7: Method to detect H. pylon GI tract infection among high-risk
patients
[00333] The objectives of this study are to assess the clinical utility of
the present
method (i) to detect active H. pylon infection compared to conventional
diagnostic tests in
patients symptomatic for peptic ulcer disease (H. pylon PUD); (ii) to confirm
eradication for
active H. pylon gastrointestinal infection compared to conventional diagnostic
tests after first-
line therapies; and (iii) assess optimal MPM thresholds distinguishing
patients with active H.
pylon PUD from those without (asymptomatic). Using this non-invasive method
allows
physicians to make effective treatment decisions without resorting to
traditional, invasive
diagnostic methods.
Study design.
[00334] The positive percent agreement (PPA) and negative percent agreement
(PA) of
the present method compared to non-serology conventional H. pylon diagnostic
tests in two
well-described adult study populations under specific test conditions are
determined as
described below herein At study entry, patients with symptomatic H. pylon PUD
meeting
clinical criteria and have at least one positive protocol-approved, non-
serology, conventional
H. pylon diagnostic test prior to any administration of primary eradication
treatment. A
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plasma test is performed on all documented symptomatic H. pylon PUD patients.
Thereafter,
these PUD patients receive a 2-4 week standard eradication regimen (as per
standard of care)
followed by 1 month drug holiday. At 30 days (+/- 3 days) after completion of
primary
treatment, all PUD patients at the end of study participation undergo a repeat
plasma test
evaluation and at least one of the original non-serology, conventional H.
pylon diagnostic
tests performed prior to treatment.
[00335] At study entry, negative control patients undergoing colonoscopy
for any
reason have no evidence of active H. pylon gastrointestinal disease based on
clinical criteria
and at least one negative protocol-approved, non-serology conventional H.
pylon diagnostic
test during screening. Thereafter, negative control colonoscopy patients have
a plasma test
performed to complete all protocol requirements.
[00336] Data from these diagnostic test comparisons provide insights as to
the clinical
utility of the present method to detect active H. pylon disease and to confirm
eradication after
primary treatment compared to non-serology conventional H. pylon diagnostic
tests.
Methods and Materials
[00337] The quantitative testing method is used to detect microorganisms
through the
analysis of non-human DNA in blood plasma. The analyte for this method is
microorganism
cell-free nucleic acid, which is very short (averaging less than 100
nucleotides in length) as
compared to human cfDNA.
[00338] Whole blood is centrifuged twice to render cell free (cf) plasma.
To address
the potential for environmental contaminants, non-volatile buffers may be
heated to a
temperature in excess of 85 C and cooled prior to use. Internal control
molecules are added
to each sample after the first centrifugation using the methods set forth in
PCT-US2017-
024176. The plasma is extracted, and purified cell free DNA (cfDNA) used to
prepare
sequencing libraries using the NEBNext DNA Library Prep Master Mix Set for
Illumina with
standard Illumina indexed adapters (purchased from IDT) and post-end repair
purification
(e.g., MagBind beads, NEBNext End Repair Module), or using a microfluidics-
based
automated library preparation platform (Mondrian ST, Ovation SP Ultralow
library system).
Adapters are ligated and purification performed without heat using AMPure
Beads, before
amplification by qPCR. Libraries are characterized using the Agilent HS
TapeStation, and
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total concentrations of nucleic acids measured to control loading volumes for
size selection
step by integrating the signal (e.g., between 50 bp and 1000 bp.
[00339] The sequenced cfDNA fragments are mapped to a reference database of
microbial sequences to determine the identity of non-human, non-internal
control material
present in the sample at significant levels (above the background of the
assay). The
sequencing data is first transformed into reads representing DNA sequences,
and then de-
multiplexed based on index sequences into collections of reads (readsets)
derived from each
library loaded onto the sequencer. Reads that align with human sequences are
filtered, and the
remaining reads that align to the sequences of internal control molecules are
set aside for
additional analyses. Next, reads matching neither the human nor internal
control references
are aligned to known microbial genomes. Reads with one or more alignments to
this database
(pathogen reads) are the basis of subsequent analysis.
[00340] The alignments of each pathogen read to the microbial genome
database are
used to infer the relative abundances of each taxon associated with the
reference sequences.
These abundances are aggregated up the taxonomy tree to give abundances at all
taxonomic
ranks. Finally, the abundances in clinical samples are compared to the
abundances in negative
control libraries on the same sequencing run to determine whether they rise
above the
background level expected due to environmental DNA contamination. Taxa meeting
this
criterion are reported in units of molecules per milliliter (MPM) based on a
ratio of the
abundance of microorganism reads and certain internal control reads obtained.
Before
resulting, the pipeline applies a set of filters to limit the reportable
organisms to those that are
greater than, for example 3-10% of the microorganism with the highest number
of reads and
greater than, for example 25-50% of any other taxonomic family related
organism. The filter
is applied for all patient samples and assay controls.
[00341] Potential sources of performance bias from sample-specific or
microbe-
specific properties include the class of microorganism-specific properties
include the class of
microorganism (e.g., bacteria, virus, eukaryote, prokaryote, fungus, etc.), GC-
content,
genome size, abundance in endogenous microflora, environmental contamination
(EC) levels,
and reference assembly number and quality of data. To address these sources of
bias, this
method includes use of a representative panel of 10-100 microorganisms that
capture the full
spectrum of potential performance bias along GC content, genome size and
strain. These
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representative organisms should span kingdoms, range in GC content (e.g., from
10%-80%),
and have genomes that range from kilobases to megabases. The representative
population
should include a mix of types, such as commensals and non-commensals, microbes
commonly found as environmental contaminants, and closely related strains. The
method
additional incorporates standard quality control measures, such as reference
intervals for
levels of microorganisms in a healthy population and EC negative controls.
[00342] The test will be considered positive if the test shows H. pylon
levels to be
significant against negative background controls. Note however, negative
percent agreement
(NPA) is not likely to be reflective of the NPA of the test after we account
for the quantitative
MPM cut-off.
[00343] In addition to assessment of PPA and NPA within each of the study
cohorts,
PUD and colonoscopy, using the threshold in MPM for positive and negative as
determined
via the lab, other thresholds in MPM will also be considered. First, MPM will
be summarized
with means, standard deviations, medians and ranges for each study cohort.
Second, receiver
operating characteristic (ROC) curves will be used to identify optimal cut
points in MPM for
maximizing PPA and NPA in the samples.
[00344] Finally, to assess the ability of present method to identify
eradication at 30
days, successful eradication will be estimated with proportions and 95%
confidence intervals
within each of the study cohorts.
Example 8. Fragment Length Distribution Profile and Site of Localization
[00345] The characteristics of the fragment length distribution of the
microbial
sequencing reads obtained from the clinical samples from patients with an
infection located in
the bloodstream and in the lungs, as an example of a deep-tissue infection
were compared.
Fragment length distribution characteristics vary depending on the site of
localization.
Without being limited by mechanism, different host responses at different
sites of infection
may contribute to varying fragment length distribution characteristics. Again
without being
limited by mechanism, different sites of infection may exhibit different non-
host nucleic acid
fragmentation mechanisms.
[00346] Clinical plasma samples: 10 deidentified clinical samples from
patients with a
confirmed bloodstream infection, and 10 deidentified clinical samples from
patients with a
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confirmed lung infection were collected. Single-centrifugation step plasma
extraction process
from whole blood within 24 hours of sample collection was performed for each
sample, as
previously described (See, Fan HC et al., PNAS 2008; 105(42): 16266-16271,
which is
incorporated by reference in its entirety herein, including any drawings), and
stored at -80 C
until use. Samples were then thawed, and 150 pL of each plasma was spiked with
1.5 pL of
Spike-in Master Mix (see below). If different volumes were obtained, a
proportionally
smaller or higher volume of Spike-in Master Mix was added to maintain a
constant
concentration of the process control molecules in all of the initial samples
and control
samples.
[00347] Negative Control Samples: Four 500 pL negative control samples (EC)
were
made from aqueous buffer (10 mM Tris pH 8, 0.1 mM EDTA, 0.05 v/v% Tween-20)
with 5
pL of Spiked-in Master Mix (see below) and served as control for environmental
contamination (i.e., microbe and pathogen nucleic acid contamination
introduced by either the
reagents, instrumentation, consumables, operators, and/or air during
processing). These
synthetic nucleic acids are later used for normalizing the signal in the
samples in order to
account for variations in sample processing.
[00348] A Spike-In Master Mix was prepared as described above herein with
ID-Spike
Molecules, SPANK molecules and SPARK molecules.
[00349] A ligation-based direct-to-library process as described in Example
1 of U.S.
Provisional App. 62/770,181 was used to prepare a sequencing library from 5 pl
spiked
asymptomatic plasma. Sequencing and sequencing data analysis was performed as
described
in Example 8.
[00350] Results: Table 2 lists all 20 clinical samples processed as part of
this example
together with the site of infection and the species of the infecting microbe
for each subject
that donated the clinical sample. The fragment length distributions for the
infecting microbes
in all the tested samples are shown in FIG. 17. The normalized fragment length
distributions
for the reads mapping to the infecting microbes' references were analyzed for
the presence of
fragment length distribution profile characteristics (e.g. short exponentially
decaying
fragments, Peak, Long fragments): (1) short exponential-like distributed
fraction ("Short" in
Table 2), (2) peak fraction ("Peak" in Table 2), and (3) Fraction of reads
longer than the read
length of the experiment (75 bp; "Long" in Table 2). Also, fractions of the
typical length
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ranges in microbial fragment length distributions. A comparison of the
fragment length
distribution profile types revealed that the infections of the bloodstream
disproprotionately
exhibit a fragment length distribution profile characterized by (1) a high
fraction of the short
pseudo-exponentially distributed fragments, (2) the absence of a peak between
20 bp and 75
bp read lengths, and (3) a fraction of long reads (> 64 bp) greater than
10%in. Conversely, the
infections of the lungs disproportionately exhibit a fragment length
distribution profile
characterized by (1) a presence of the short pseudo-exponentially distributed
fragments, (2)
the presence of a peak between 20 bp and 75 bp read lengths, and (3) fraction
of the long
reads smaller than 10%. This suggests that the features of the microbial
fragment length
distributions can be used to determine whether the infection is present in the
bloodstream or in
deep tissue.
[00351] Table 2 List of the clinical samples and the site of infection, the
species of
the infecting microbe, and the properties of the fragment length distribution
for the
sequencing reads mapping to the reference of the infecting microbial species.
For each
property, its qualitative assessment (present/absent) is indicated and the
fraction of total
reads that exists in that segment is given in parentheses. Here, the short
fragment
section includes reads from 22 bp up to and including 29 bp; the Peak fragment
length
range includes reads from 30 bp up to and including 59 bp; and long fragment
range
includes reads longer than 59 bp.
Sample ID Site of Species of the Infecting
Fragment Length Distribution Features
Infection Microbe Short Peak Long
RD-19543 Lungs Pseudomonas aeruginosa Present (0.14)
(0.50) Present (0.36)
RD-19553 Streptococcus pyogenes Absent (0.36) Present (0.84)
Absent (0.12)
RD-19554 Candida parapsilosis Absent (0.08) Present (0.85)
Absent (0.07)
RD-19557 Enterococcus faecalis Absent (0.05) Present (0.69)
Absent (0.26)
RD-19539 Candida dubliniensis Absent (0.09) Present (0.51)
Present (0.40)
RD-19552 Staphylococcus epidermidis Present (0.21) Present
(0.73) Absent (0.06)
RD-19556 Staphylococcus epidermidis Present (0.14) Present
(0.70) Absent (0.16)
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Sample ID Site of Species of the Infecting
Fragment Length Distribution Features
Infection Microbe Short Peak Long
RD-19555 Escherichia coli Present (0.10) Present (0.71) Absent
(0.19)
RD-19550 Stenotrophomonas maltophilia Present (0.16) Present
(0.68) Absent (0.16)
RD-19540 Staphylococcus aureus Present (0.34) Present (0.63)
Absent (0.03)
RD-19544 Bloodstream Staphylococcus aureus Present (0.42)
Absent (0.56) Absent (0.02)
RD-19548 Staphylococcus epidermidis Present (0.14)
Absent (0.50) Present (0.36)
RD-19547 Candida parapsilosis Present (0.15) Absent (0.67)
Present (0.18)
RD-19545 Pseudomonas aeruginosa Present (0.14)
Absent (0.49) Present (0.37)
RD-19551 Escherichia coli Absent (0.04) Present (0.90) Absent
(0.07)
RD-19542 Streptococcus pyogenes Absent (0.05) Present (0.88)
Absent (0.07)
RD-19546 Staphylococcus epidermidis Absent (0.01) Present
(0.86) Absent (0.13)
RD-19541 Candida dubliniensis Absent (0.09) Present (0.68)
Present (0.23)
RD-19558 Enterococcus faecalis Present (0.17) Present (0.69)
Absent (0.14)
RD-19546 Stenotrophomonas maltophilia Present (0.12) Present
(0.66) Absent (0.22)
Example 9. Fragment Length Distribution Profile and Site of Localization 2
[00352] The characteristics of the fragment length distribution of the
microbial
sequencing reads obtained from the clinical samples from patients with an
infection located in
the bloodstream (plasma from a venous blood draw) and plasma from capillary
blood that
came into contact with the skin on the fingertip prior to collecting it in the
capillary draw
collection system, as an example of a skin infection were compared.
[00353] Clinical plasma samples: Blood from 20 healthy adult donors was
collected
into a PPT tubes with K2EDTA as the anticoagulant (Becton Dickinson, Franklin
Lakes, NJ)
according to the manufacturer's instructions. Immediately following the venous
blood draw, a
capillary blood draw was performed on the same group of 20 healthy donors
using Microvette
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CB300 blood sampling devices using K2EDTA as the anticoagulant (Sarstedt Inc,
Sparks,
NV). The following procedure was applied during the capillary draw: (1) The
donor's
finger was held in an upward position and lanced the palm-side surface of the
finger with
proper-size lancet, (2) Pressing firmly on the finger when making the puncture
was avoided to
prevent hemolysis of the drawn blood, and (3) the blood droplet spread over
the fingertip was
collected into a clean Microvette CB300 blood sampling device. A single-
centrifugation step
plasma extraction process from whole blood within 12 hours of sample
collection was
performed for each sample according to the manufacturer's instructions, and
plasma stored at
-80 C until use. Samples were then thawed, and each plasma was spiked with
the volume of
Spike-in Master Mix equal to 1% of the plasma volume.
[00354] Negative Control Samples: Four 500 pL negative control samples (EC)
were
made from aqueous buffer (10 mM Tris pH 8, 0.1 mM EDTA, 0.05 v/v% Tween-20)
with 5
pL of Spiked-in Master Mix (see below) and served as control for environmental
contamination (i.e., microbe and pathogen nucleic acid contamination
introduced by either the
reagents, instrumentation, consumables, operators, and/or air during
processing). These
synthetic nucleic acids are later used for normalizing the signal in the
samples in order to
account for variations in sample processing.
[00355] Negative Microvette Samples: Four 300 pL of aqueous buffer (10 mM
Tris pH
8, 0.1 mM EDTA, 0.05 v/v% Tween-20) was added into four clean and unused
Microvette
CB300 blood sampling devices and incubated for 6 hours at room temperature
before
collecting quantitatively the content and spiking with 3 pL of Spiked-in
Master Mix (see
below).
[00356] A Spike-In Master Mix was prepared as described above herein with
ID-Spike
Molecules, Spank molecules and Spark molecules.
[00357] Direct-from-plasma nucleic acid library generation: 25.0 pL of each
spiked
sample was mixed with 10.0 pL of 10x Terminal Transferase Reaction Buffer
(NEB, Ipswich,
MA), 2.5 pL of Proteinase K (Sigma), 1.0 pL of 10% Tween-20 (Thermo-Fisher
Scientific,
Waltham, MA), 1.0 pL of 10% Triton X100 (Thermo-Fisher Scientific, Waltham,
MA) and
60.5.0 pL Nuclease-free water. The mixture was heated to 60 C for 20 minutes
and 95 C for
minutes and placed on ice until cool. 1.0 pL of 10 mM dATP, 1.0 pL Terminal
Transferase (20 u/ pL, NEB, Ipswich, MA) and 3.0 pL Nuclease-free water was
added to
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prepare the A-tailing reaction which was incubated at 37 C for 40 mm. 150.0
pL of
Lysis/Binding Buffer (Thermo-Fisher Scientific, Waltham, MA) was added to the
reaction.
The entire volume was then added to 25.0 pL of Dynabeads oligo (dT)25 (Thermo-
Fisher
Scientific, Waltham, MA), which had been washed once with Lysis/Binding Buffer
(Thermo-
Fisher Scientific, Waltham, MA). The mixture was incubated at 25 C and 600
RPM. The
remainder of the procedure followed the steps of the protocol outlined in
Example 1.
[00358] Sequencink: The samples were sequenced to obtain sequence reads
using a
NextSeqTM 500 sequencer by Illumina. Sequencing was conducted following the
manufacturer's instructions. The sequencing analysis was performed as
described above in
Example 1.
[00359] Results: FIG. 18A shows a normalized fragment length distribution
of the
microbes detected in the venous draw of two of the donors of this study, and
FIG 18B shows
the normalized fragment length distributions of the microbes detected in the
one of the
replicate capillary draws from the same two donors. The two microbes detected
in the venous
draws (e.g. Haemophilus influenzae in Donor 1, and Streptococcus the rmophilus
in Donor 2)
were detected in the biological samples obtained during the capillary draw
collection process
as well and showed a similar fragment length distribution in both collection
types, i.e. a
peaked fragment length distribution (FIG 18A and FIG 18B). The additional
microbes
detected in the samples obtained with the process applied during the capillary
draw included a
more diverse set of microbes (Table 3). The majority of these additional
microbes co-occur in
both replicates per each donor (FIG. 18C). In order to confirm that these
additional microbes
are not contributed by the contamination present in the Microvette CB300 blood
sampling
devices used to collect the samples obtained from the procedure applied during
the capillary
draw or derived from the process contamination, we analyzed the sequencing
data obtained
from the Negative Microvette Samples (see above). FIG. 18D shows the
comparison of the
abundance in units of MPM for the additional microbes in the biological sample
obtained
from the process applied during the capillary blood draw (x-axis) and the
abundance in units
of MPM for the same microbes in the Negative Microvette Samples. The vast
majority of the
signal of the additional microbes in the data obtained from the capillary draw
is not
contributed by the tube contamination profile, and can be concluded to have
derived from the
biological sample obtained by collecting the blood drop from the fingertip. As
the signal for
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these microbes was not detected in the venous draw, they must have originated
from the skin
surface over which the blood spread after the fingertip skin was lanced,
suggesting that the
skin-derived microbial nucleic acids show different properties of their
fragment length
distributions, e.g. the absence of a peak between 20 bp and 75 bp, and an
exponential-like
decay in the frequency of the fragments with fragment length. The same trends
are observed
in the other sample donors (data not shown).
[00360] Table 3. List of Microbial species Detected in the biological
sample
obtained from the process applied during the capillary blood draw for Donor 1
and
Donor 2.
Microbial species detected in Donor 1 Microbial species detected in Donor
2
Alternaria arborescens Acinetobacter baumannii
B acteroides stercoris Bacteroides ovatus
B acteroides uniformis Bacteroides stercoris
B acteroides vulgatus Bacteroides uniformis
Corynebacterium afermentans Bacteroides vulgatus
Corynebacterium amycolatum Corynebacterium aurimucosum
Corynebacterium aurimucosum Corynebacterium simulans
Dermabacter hominis Corynebacterium tuscaniense
Finegoldia magna Facklamia hominis
Gardnerella vaginalis Finegoldia magna
Lactobacillus iners Lactobacillus crispatus
Malassezia globosa Moraxella catarrhalis
Micrococcus lylae Oligella urethralis
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Microbial species detected in Donor 1 Microbial species detected in Donor
2
Peptoniphilus rhinitidis Peptoniphilus harei
Propionibacterium granulosum Saccharomyces cerevisiae
Rhodococcus fascians Staphylococcus capitis
Staphylococcus capitis Staphylococcus epidermidis
Staphylococcus epidermidis Staphylococcus warneri
Staphylococcus hominis Streptococcus mitis
Streptococcus mitis Streptococcus thermophilus
Streptococcus thermophilus
Streptococcus tigurinus
Example 10. Infection Post Transplant
[00361] 10 transplant patients are monitored for possible infections post-
transplant
surgery, and the pathogens detected at the pre-symptomatic stage are monitored
for the
changes in their fragment length distribution to correlate the stage of
infection with the
observed fragment length. In particular, the presence of the peak between 20
bp and 75 bp as
well as the fraction of fragments not associated with the peak is tracked as
the infection
progresses through different stages. In addition to these 10 transplant
patients, 10
deindentified serial sampling sets from Karius production are selected in
order to track the
same behavior.
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Example 11. Site of Localization Assessment
[00362] 1000 deidentified samples from Karius production are spiked and
processed
along with the Assay Controls and Environmental Controls using template-
switching based
direct-to-library method with Proteinase K as described in U.S. Provisional
62/770,181. The
1000 deidentified samples include plasma samples from patients that have
pneumonia,
immunocompromised status, endocarditis, sepsis, or invasive fungal infection.
icrobial
abundance and microbial and host fragment length distributions are analyzed in
order to relate
features of the fragment length distributions (e.g. the presence or absence of
the peak between
20 and 75 bp, the fraction of the reads longer than 65 bp, the fraction of the
reads shorter than
40 bp) to the site of infection, in particular related to the presence of the
peak in either a deep
tissue infection or commensal.
Example 12. Infection Stage Determination from Microbial Fragment Length
Distribution
[00363] In order to determine the fragment length profile diagnostic
predictability
value for measuring a stage of infection, a set of clinical plasma samples
were collected from
16 different consented subjects suspected of having an infection by drawing
blood into PPT
tubes and extracting plasma by a single centrifugation step according to the
manufacturer's
recommendations. The plasma samples were shipped frozen or at ambient
temperature
overnight to Karius lab in Redwood City, CA. For each subject, the first
sample was obtained
at the point of hospital admission at which point an orthogonal test (e.g.
blood culture) was
also performed to confirm the likely microbe species responsible or in part
responsible for the
infection. Subsequently, additional samples were drawn from the subjects at
various time
points during treatment to monitor the progress of infection and treatment
effects. In total, the
samples were collected at least at two time points per subject, including the
time point of
admission. The maximum number of time points per subject was 7. Plasma samples
and
Negative Control Samples were processed to nucleic acid libraries and
sequenced as
described above.
[00364] The group of subjects of this study included 3 patients
orthogonally diagnosed
with bloodstream infections, 8 patients orthogonally diagnosed with
endocarditis, and 5
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patients orthogonally diagnosed as febrile neutropenic patients. FIG. 19A,
19B, and 19C
show changes in the fragment length distribution in a representative case of a
bloodstream
infection, endocarditis, and febrile neutropenia, respectively. The example
fragment length
distributions in FIG. 19 indicate high probability for short exponentially
distributed fragments
(the range < 40 bp), and increased probability for the peaked distribution
around 50 bp after
the treatment has started. The fraction of the short exponentially distributed
or close-to-
exponentially distributed fragments was therefore studied in all the processed
samples. FIG.
20A depicts the kinetics of the changes in this short read fraction. This
suggests that an
invasive infection can be diagnosed based on the presence of the short and
exponentially
distributed read fraction, especially in the case of a bloodstream infection
or bacteremia. In a
single subject a high fraction of reads > 64 bp was present, possibly
indicating saturation of
the mechanism that yields the short exponentially distributed fraction (data
not shown). A
concurrent measurement of microbial abundances (FIG. 20B) enables the
determination of
the infection stage by a combined use of abundance and fragment length profile
measurements.
[00365] The sequencing data also indicates the presence of microbes not
orthogonally
confirmed by other microbiological tests performed. The fragment length
distribution can be
studied also in the case of these microbes. For example, Haemophilus
influenzae and
Prevotella melaninogenica were detected by the disclosed method in the
admission samples
from the subjects RD-06 and RD-13, respectively (FIG. 21A). While the
orthogonally
detected microbe, the presumed cause of the infection showed high short read
fraction in both
cases, the additional microbes showed variable trends; Haemophilus influenzae
fragment
length distribution was consistent with an invasive or bacteremic infection
while Prevotella
melaninogenica showed only the presence of a peaked distribution, consistent
with an
invisible stage of infection or commensal behaviour in asymptomatic patients
(see e.g.
Helicobacter pylori fragment length distribution in the U.S Provisional
Application
No.62/770,181, titled "Direct-to-Library Methods, Systems and Compositions",
filed
November 21, 2018) or managed infection footprint. In addition, new microbes
can emerge
during the course of treatment, and fragment length analysis may assist in
diagnosing the
infection state of these as well. For example, FIG. 21B shows the fragment
length
distributions of the reads aligning to Enterococcus gallinarum, which show a
detectable
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fraction of short exponentially distributed reads with a string peak fraction.
The decision to
treat this infection may be based on the magnitude of the short read fraction.
The inspection of
the clinical records confirmed that the subject was indeed treated for this
infection.
[00366] Finally, the changes in the human fragment length distribution were
analyzed
as the studied subjects moved through the infection cycle from the symptomatic
stage of
infection at admission and diagnosis and through the treatment stage of the
infection during
therapy. FIG. 22 depicts the main three modes of behaviour of the human
fragment
distributions in infected patients studied here: (1) the fraction of the long
(mainly
nucleosomal) human reads decrease during treatment (left panel in FIG. 22,
37.5% of total
subjects in this study), (2) the fraction of the long human reads fluctuate
during treatment
(middle panel in FIG. 22, 37.5% of total subjects in this study), and (3) the
fraction of the
long (mainly nucleosomal) human reads increase during treatment (right panel
in FIG. 22,
37.5% of total subjects in this study 25%). As shown above, human fragment
length
distribution shape and properties can be predictive of an infection stage of a
subject. The
parameters derived from the human distribution can then be used in combination
with the
fragment length of the infecting microbe or other microbes detected in the
sample to predict
the recovery trajectory in a subject, e.g. if the subject is recovering, if
another microbe infects
a subject during the treatment for the initial infection, or recognize and
invisible infection or
commensal presence.
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